Professor Josef Kittler

Research Interests

I have worked on various theoretical aspects of Pattern Recognition, Image Analysis and Computer Vision, and on many applications including System Identification, Automatic Inspection, ECG diagnosis, Mammographic Image Interpretation, Remote Sensing, Robotics, Speech Recognition, Character Recognition and Document Processing, Image Coding, Biometrics, Image and Video Database Retrieval, Surveillance. Contributions to statistical pattern recognition include k-nearest neighbour methods of pattern classification, feature selection, contextual classification, probabilistic relaxation and most recently to multiple expert fusion. In computer vision my major contributions include robust statistical methods for shape analysis and detection, motion estimation and segmentation, and image segmentation by thresholding and edge detection.

I have co-authored a book with the title `Pattern Recognition: a statistical approach' published by Prentice-Hall and  published more than 500 papers.

Departmental Duties

  •  Programme Director for the EuroMaster MSc Programmes

Affiliations

Served as a member of the Editorial Board of IEEE Transactions on Pattern Analysis and Machine Intelligence during 1982-85. Currently serves on the Editorial Boards of Pattern Recognition Letters, Pattern Recognition and Artificial Intelligence, Pattern Analysis and Applications.

Served on the Governing Board of the International Association for Pattern Recognition (IAPR) as one of the two British representatives during the period 1982-2005. President of the IAPR during 1994-1996. Currently a member of the KS Fu Prize Committee of IAPR.

Achievements and Honours

Received Best Paper awards from the Pattern Recognition Society, the British Machine Vision Association and IEE.

Received ``Honorary Medal'' from the Electrotechnical Faculty of the Czech Technical University in Prague in September 1995 for contributions to the field of pattern recognition and computer vision.

Elected Fellow of the International Association for Pattern Recognition in 1998

Elected Fellow of Institution of Electrical Engineers in 1999

Received Honorary Doctorate from the Lappeenranta University of Technology, Finland, for contributions to Pattern Recognition and Computer Vision in 1999

Elected Fellow of the Royal Academy of Engineering, 2000

Received Institution of Electrical Engineers Achievemments Medal 2002 for outstanding contributions to Visual Information Engineering

Elected BMVA Distinguished Fellow 2002

Received, from the Czech Academy of Sciences, the 2003 Bernard Bolzano Honorary Medal for Merit in the Mathematical Sciences

Awarded the title Distinguished Professor of the University of Surrey in 2004

Appointed as Series Editor for Springer Lecture Notes in Computer Science 2004

Awarded the KS Fu Prize 2006, by the International Association for Pattern Recognition, for outstanding contributions to Pattern Recognition (the prize awarded biennially)

Received Honorary Doctorate from the Czech Technical University in Prague in 2007, on the occasion of the 300th anniversary of its foundation.

Awarded the IET Faraday Medal 2008.

Contact Me

E-mail:
Phone: 01483 68 9294

Find me on campus
Room: 25 BA 00

Publications

Highlights

  • Ortega-Garcia J, Fierrez J, Alonso-Fernandez F, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro J-L, Gonzalez-Agulla E, Otero-Muras E, Garcia-Salicetti S, Allano L, Ly-Van B, Dorizzi B, Kittler J, Bourlai T, Poh N, Deravi F, Ng MWR, Fairhurst M, Hennebert J, Humm A, Tistarelli M, Brodo L, Richiardi J, Drygajlo A, Ganster H, Sukno FM, Pavani S-K, Frangi A, Akarun L, Savran A. (2010) 'The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 32 (6), pp. 1097-1111.
  • Kim T-K, Kittler J, Cipolla R. (2010) 'On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON IMAGE PROCESSING, 19 (4), pp. 1067-1074.
  • Yan F, Christmas W, Kittler J. (2008) 'Layered data association using graph-theoretic formulation with applications to tennis ball tracking in monocular sequences'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Minneapolis, MN: 30 (10), pp. 1814-1830.
  • Ilonen J, Kamarainen J-K, Paalanen P, Hamouz M, Kittler J, Kalviainen H. (2008) 'Image feature localization by multiple hypothesis testing of Gabor features'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON IMAGE PROCESSING, 17 (3), pp. 311-325.

Journal articles

  • Tahir MA, Kittler J, Bouridane A. (2016) 'Multi-label classification using stacked spectral kernel discriminant analysis'. ELSEVIER SCIENCE BV NEUROCOMPUTING, 171, pp. 127-137.
  • Poh N, Kittler J, Chan C-H, Pandit M. (2015) 'Algorithm to estimate biometric performance change over time'. INST ENGINEERING TECHNOLOGY-IET IET BIOMETRICS, 4 (4), pp. 236-245.
  • Feng Z-H, Hu G, Kittler J, Christmas W, Wu X-J. (2015) 'Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON IMAGE PROCESSING, 24 (11), pp. 3425-3440.
  • Arashloo SR, Kittler J, Christmas W. (2015) 'Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 10 (11), pp. 2396-2407.
  • Windridge D, Kittler J, de Campos T, Yan F, Christmas W. (2015) 'A Novel Markov Logic Rule Induction Strategy for Characterizing Sports Video Footage'. IEEE COMPUTER SOC IEEE MULTIMEDIA, 22 (2), pp. 24-35.
  • Kiliç V, Barnard M, Wang W, Kittler J. (2015) 'Audio assisted robust visual tracking with adaptive particle filtering'. IEEE Transactions on Multimedia, 17 (2), pp. 186-200.

    Abstract

    © 1999-2012 IEEE. The problem of tracking multiple moving speakers in indoor environments has received much attention. Earlier techniques were based purely on a single modality, e.g., vision. Recently, the fusion of multi-modal information has been shown to be instrumental in improving tracking performance, as well as robustness in the case of challenging situations like occlusions (by the limited field of view of cameras or by other speakers). However, data fusion algorithms often suffer from noise corrupting the sensor measurements which cause non-negligible detection errors. Here, a novel approach to combining audio and visual data is proposed. We employ the direction of arrival angles of the audio sources to reshape the typical Gaussian noise distribution of particles in the propagation step and to weight the observation model in the measurement step. This approach is further improved by solving a typical problem associated with the PF, whose efficiency and accuracy usually depend on the number of particles and noise variance used in state estimation and particle propagation. Both parameters are specified beforehand and kept fixed in the regular PF implementation which makes the tracker unstable in practice. To address these problems, we design an algorithm which adapts both the number of particles and noise variance based on tracking error and the area occupied by the particles in the image. Experiments on the AV16.3 dataset show the advantage of our proposed methods over the baseline PF method and an existing adaptive PF algorithm for tracking occluded speakers with a significantly reduced number of particles.

  • Feng ZH, Huber P, Kittler J, Christmas W, Wu XJ. (2015) 'Random cascaded-regression copse for robust facial landmark detection'. IEEE Signal Processing Letters, 22 (1), pp. 76-80.
  • Arashloo SR, Kittler J. (2014) 'Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE Transactions on Information Forensics and Security, 9 (12), pp. 2100-2109.
  • Arashloo SR, Kittler J. (2014) 'Dynamic Texture Recognition Using Multiscale Binarized Statistical Image Features'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON MULTIMEDIA, 16 (8), pp. 2099-2109.
  • Veta M, Diest PJV, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, Gonzalez F, Larsen ABL, Vestergaard JS, Dahl AB, Cireşan DC, Schmidhuber J, Giusti A, Gambardella LM, Tek FB, Walter T, Wang C-W, Kondo S, Matuszewski BJ, Precioso F, Snell V, Kittler J, Campos TED, Khan AM, Rajpoot NM, Arkoumani E, Lacle MM, Viergever MA, Pluim JPW. (2014) 'Assessment of algorithms for mitosis detection in breast cancer histopathology images'.
  • Yan F, Kittler J, Windridge D, Christmas W, Mikolajczyk K, Cox S, Huang Q. (2014) 'Automatic annotation of tennis games: An integration of audio, vision, and learning'. Image and Vision Computing, 32 (11), pp. 896-903.
  • Kittler J, Kompatsiaris I, Malasiotis S, Daras P, Grammalidis N, Mezaris V, Manakos I, Tzovaras D. (2014) 'Professor Maria Petrou's Professional Career'. ELSEVIER SCIENCE BV PATTERN RECOGNITION LETTERS, 48, pp. 100-102.
  • Arashloo SR, Kittler J. (2014) 'Fast pose invariant face recognition using super coupled multiresolution Markov Random Fields on a GPU'. ELSEVIER SCIENCE BV PATTERN RECOGNITION LETTERS, 48, pp. 49-59.
  • Kilic V, Zhong X, Barnard M, Wang W, Kittler J. (2014) 'Audio-Visual Tracking of a Variable Number of Speakers with a Random Finite Set Approach'. IEEE 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), Salamanca, SPAIN:
  • Barnard M, Koniusz P, Wang W, Kittler J, Naqvi SM, Chambers J. (2014) 'Robust multi-speaker tracking via dictionary learning and identity modeling'. IEEE Transactions on Multimedia, 16 (3), pp. 864-880.
  • Khan A, Windridge D, Kittler J. (2014) 'Multilevel Chinese Takeaway Process and Label-Based Processes for Rule Induction in the Context of Automated Sports Video Annotation'. Cybernetics, IEEE Transactions on, PP Article number 99 , pp. 1-1.

    Abstract

    We propose four variants of a novel hierarchical hidden Markov models strategy for rule induction in the context of automated sports video annotation including a multilevel Chinese takeaway process (MLCTP) based on the Chinese restaurant process and a novel Cartesian product label-based hierarchical bottom-up clustering (CLHBC) method that employs prior information contained within label structures. Our results show significant improvement by comparison against the flat Markov model: optimal performance is obtained using a hybrid method, which combines the MLCTP generated hierarchical topological structures with CLHBC generated event labels. We also show that the methods proposed are generalizable to other rule-based environments including human driving behavior and human actions.

  • Liu Q, Wang W, Jackson PJB, Barnard M, Kittler J, Chambers J. (2013) 'Source separation of convolutive and noisy mixtures using audio-visual dictionary learning and probabilistic time-frequency masking'. IEEE Transactions on Signal Processing, 61 (22) Article number 99 , pp. 5520-5535.
  • McIntyre AH, Hancock PJB, Kittler J, Langton SRH. (2013) 'Improving Discrimination and Face Matching with Caricature'. WILEY-BLACKWELL APPLIED COGNITIVE PSYCHOLOGY, 27 (6), pp. 725-734.
  • Kittler J, Christmas W, de Campos T, Windridge D, Yan F, Illingworth J, Osman M. (2013) 'Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy.'. IEEE Trans Pattern Anal Mach Intell,

    Abstract

    We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifaceted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system.

  • Chan CH, Tahir MA, Kittler J, Pietikäinen M. (2013) 'Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors.'. IEEE Trans Pattern Anal Mach Intell, United States: 35 (5), pp. 1164-1177.
  • Mendez-Vázquez H, Kittler J, Chan CH, García-Reyes E. (2013) 'Photometric normalization for face recognition using local discrete cosine transform'. International Journal of Pattern Recognition and Artificial Intelligence, 27 (3)
  • Poh N, Ross A, Li W, Kittler J. (2013) 'Corrigendum to "A user-specific and selective multimodal biometric fusion strategy by ranking subjects" [Pattern Recognition 46 (2013) 3341-3357] (DOI:10.1016/j.patcog.2013.03.018)'. Pattern Recognition,
  • Snell V, Christmas W, Kittler J. (2013) 'HEp-2 fluorescence pattern classification'. Pattern Recognition,
  • Snell V, Christmas W, Kittler J. (2013) 'HEp-2 fluorescence pattern classification'. ELSEVIER SCI LTD PATTERN RECOGNITION, 47 (7), pp. 2338-2347.
  • Tahir M, Yan F, Koniusz P, Awais M, Barnard M, Mikolajczyk K, Kittler J. (2012) 'A Robust and Scalable Visual Category and Action Recognition System using Kernel Discriminant Analysis with Spectral Regression'. IEEE Transactions on Multimedia,
    [ Status: Accepted ]
  • Sidiropoulos P, Mezaris V, Kompatsiaris IY, Kittler J. (2012) 'Differential Edit Distance: A Metric for Scene Segmentation Evaluation'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 22 (6), pp. 904-914.
  • Tahir MA, Kittler J, Bouridane A. (2012) 'Multilabel classification using heterogeneous ensemble of multi-label classifiers'. PATTERN RECOGNITION LETTERS, 33 (5), pp. 513-523.
  • Merati A, Poh N, Kittler J. (2012) 'User-specific cohort selection and score normalization for biometric systems'. IEEE Transactions on Information Forensics and Security, 7 (4), pp. 1270-1277.
  • Tahir MA, Kittler J, Yan F. (2012) 'Inverse random under sampling for class imbalance problem and its application to multi-label classification'. Elsevier Pattern Recognition, 45 (10), pp. 3738-3750.
  • Poh N, Kittler J, Alkoot F. (2012) 'A discriminative parametric approach to video-based score-level fusion for biometric authentication'. Proceedings - International Conference on Pattern Recognition, , pp. 2335-2338.
  • Chan CH, Kittler J. (2012) 'BLUR KERNEL ESTIMATION TO IMPROVE RECOGNITION OF BLURRED FACES'. IEEE 2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), Lake Buena Vista, FL: , pp. 1989-1992.
  • Chan CH, Goswami B, Kittler J, Christmas W. (2012) 'Local ordinal contrast pattern histograms for spatiotemporal, lip-based speaker authentication'. IEEE Transactions on Information Forensics and Security, 7 (2), pp. 602-612.

    Abstract

    Lip region deformation during speech contains biometric information and is termed visual speech. This biometric information can be interpreted as being genetic or behavioral depending on whether static or dynamic features are extracted. In this paper, we use a texture descriptor called local ordinal contrast pattern (LOCP) with a dynamic texture representation called three orthogonal planes to represent both the appearance and dynamics features observed in visual speech. This feature representation, when used in standard speaker verification engines, is shown to improve the performance of the lip-biometric trait compared to the state-of-the-art. The best baseline state-of-the-art performance was a half total error rate (HTER) of 13.35% for the XM2VTS database. We obtained HTER of less than 1%. The resilience of the LOCP texture descriptor to random image noise is also investigated. Finally, the effect of the amount of video information on speaker verification performance suggests that with the proposed approach, speaker identity can be verified with a much shorter biometric trait record than the length normally required for voice-based biometrics. In summary, the performance obtained is remarkable and suggests that there is enough discriminative information in the mouth-region to enable its use as a primary biometric trait. © 2006 IEEE.

  • Yan F, Kittler J, Mikolajczyk K, Tahir A. (2011) 'Non-Sparse Multiple Kernel Fisher Discriminant Analysis'. Microtome Publishing Journal of Machine Learning Research, 13, pp. 607-642.

    Abstract

    Sparsity-inducing multiple kernel Fisher discriminant analysis (MK-FDA) has been studied in the literature. Building on recent advances in non-sparse multiple kernel learning (MKL), we propose a non-sparse version of MK-FDA, which imposes a general `p norm regularisation on the kernel weights. We formulate the associated optimisation problem as a semi-infinite program (SIP), and adapt an iterative wrapper algorithm to solve it. We then discuss, in light of latest advances inMKL optimisation techniques, several reformulations and optimisation strategies that can potentially lead to significant improvements in the efficiency and scalability of MK-FDA. We carry out extensive experiments on six datasets from various application areas, and compare closely the performance of `p MK-FDA, fixed norm MK-FDA, and several variants of SVM-based MKL (MK-SVM). Our results demonstrate that `p MK-FDA improves upon sparse MK-FDA in many practical situations. The results also show that on image categorisation problems, `p MK-FDA tends to outperform its SVM counterpart. Finally, we also discuss the connection between (MK-)FDA and (MK-)SVM, under the unified framework of regularised kernel machines.

  • Arashloo SR, Kittler J, Christmas WJ. (2011) 'Pose-invariant face recognition by matching on multi-resolution MRFs linked by supercoupling transform'. ACADEMIC PRESS INC ELSEVIER SCIENCE COMPUTER VISION AND IMAGE UNDERSTANDING, 115 (7), pp. 1073-1083.
  • Poh N, Kittler J. (2011) 'A Unified Framework for Multimodal Biometric Fusion Incorporating Quality Measures.'. IEEE Trans Pattern Anal Mach Intell,
  • Kittler J, Poh N, Merati A. (2011) 'Cohort based approach to multiexpert class verification'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6713 LNCS, pp. 319-329.
  • Kim T-K, Stenger B, Kittler J, Cipolla R. (2011) 'Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications'. SPRINGER INTERNATIONAL JOURNAL OF COMPUTER VISION, 91 (2), pp. 216-232.
  • Poh N, Chan CH, Kittler J, Marcel S, Mc Cool C, Argones Rua E, Alba Castro JL, Villegas M, Paredes R, Struc V, Pavesic N, Salah AA, Fang H, Costen N. (2010) 'An Evaluation of Video-to-Video Face Verification'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 5 (4), pp. 781-801.
  • Rahimzadeh Arashloo S, Kittler J. (2010) 'Energy Normalization for Pose-Invariant Face Recognition Based on MRF Model Image Matching.'. IEEE Trans Pattern Anal Mach Intell,
  • Arturo Olvera-Lopez J, Ariel Carrasco-Ochoa J, Francisco Martinez-Trinidad J, Kittler J. (2010) 'A review of instance selection methods'. SPRINGER ARTIFICIAL INTELLIGENCE REVIEW, 34 (2), pp. 133-143.
  • Bourlai T, Kittler J, Messer K. (2010) 'On design and optimization of face verification systems that are smart-card based'. SPRINGER MACHINE VISION AND APPLICATIONS, 21 (5), pp. 695-711.
  • Ortega-Garcia J, Fierrez J, Alonso-Fernandez F, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro J-L, Gonzalez-Agulla E, Otero-Muras E, Garcia-Salicetti S, Allano L, Ly-Van B, Dorizzi B, Kittler J, Bourlai T, Poh N, Deravi F, Ng MWR, Fairhurst M, Hennebert J, Humm A, Tistarelli M, Brodo L, Richiardi J, Drygajlo A, Ganster H, Sukno FM, Pavani S-K, Frangi A, Akarun L, Savran A. (2010) 'The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 32 (6), pp. 1097-1111.
  • Poh N, Kittler J, Bourlai T. (2010) 'Quality-Based Score Normalization With Device Qualitative Information for Multimodal Biometric Fusion'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 40 (3), pp. 539-554.
  • Kim T-K, Kittler J, Cipolla R. (2010) 'On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON IMAGE PROCESSING, 19 (4), pp. 1067-1074.
  • Poh N, Bourlai T, Kittler J. (2010) 'A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms'. ELSEVIER SCI LTD PATTERN RECOGNITION, 43 (3), pp. 1094-1105.
  • Tahir MA, Yan F, Barnard M, Awais M, Mikolajczyk K, Kittler J. (2010) 'The University of Surrey visual concept detection system at ImageCLEF@ICPR: Working notes'. Springer Lecture Notes in Computer Science: Recognising Patterns in Signals, Speech, Images and Videos, 6388, pp. 162-170.
  • Sadeghi MT, Samiei M, Kittler J. (2010) 'Fusion of PCA-Based and LDA-Based Similarity Measures for Face Verification'. HINDAWI PUBLISHING CORPORATION EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, Article number ARTN 647597
  • Windridge D, Kittler J. (2010) 'Perception-Action Learning as an Epistemologically-Consistent Model for Self-Updating Cognitive Representation'. SPRINGER-VERLAG BERLIN BRAIN INSPIRED COGNITIVE SYSTEMS 2008, Sao Luis, BRAZIL: 657, pp. 95-134.
  • Poh N, Bourlai T, Kittler J, Allano L, Alonso-Fernandez F, Ambekar O, Baker J, Dorizzi B, Fatukasi O, Fierrez J, Ganster H, Ortega-Garcia J, Maurer D, Salah AA, Scheidat T, Vielhauer C. (2009) 'Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 4 (4), pp. 849-866.
  • Shevchenko M, Windridge D, Kittler J. (2009) 'A linear-complexity reparameterisation strategy for the hierarchical bootstrapping of capabilities within perception-action architectures'. ELSEVIER SCIENCE BV IMAGE AND VISION COMPUTING, 27 (11), pp. 1702-1714.
  • Bourlai T, Kittler J, Messer K. (2009) 'Designing a smart-card-based face verification system: empirical investigation'. SPRINGER MACHINE VISION AND APPLICATIONS, 20 (4), pp. 225-242.
  • Granai L, Tena JR, Hamouz M, Kittler J. (2009) 'Influence of compression on 3D face recognition'. ELSEVIER SCIENCE BV PATTERN RECOGNITION LETTERS, 30 (8), pp. 745-750.
  • Olvera-Lopez JA, Martinez-Trinidad JF, Carrasco-Ochoa JA, Kittler J. (2009) 'Prototype selection based on sequential search'. IOS PRESS INTELLIGENT DATA ANALYSIS, 13 (4), pp. 599-631.
  • Loog M, Wu X-J, Lu J-P, Yang J-Y, Wang S-T, Kittler J. (2008) 'A note on an extreme case of the generalized optimal discriminant transformation'. ELSEVIER SCIENCE BV NEUROCOMPUTING, 72 (1-3), pp. 664-665.
  • Yan F, Christmas W, Kittler J. (2008) 'Layered data association using graph-theoretic formulation with applications to tennis ball tracking in monocular sequences'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Minneapolis, MN: 30 (10), pp. 1814-1830.
  • Roh M-C, Christmas B, Kittler J, Lee S-W. (2008) 'Gesture spotting for low-resolution sports video annotation'. PATTERN RECOGNITION, 41 (3), pp. 1124-1137.
  • Poh N, Kittler J. (2008) 'Incorporating model-specific score distribution in speaker verification systems'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 16 (3), pp. 594-606.
  • Ilonen J, Kamarainen J-K, Paalanen P, Hamouz M, Kittler J, Kalviainen H. (2008) 'Image feature localization by multiple hypothesis testing of Gabor features'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON IMAGE PROCESSING, 17 (3), pp. 311-325.
  • Gonzalez-Jimenez D, Argones-Rua E, Alba-Castro JL, Kittler J. (2007) 'Evaluation of point localisation and similarity fusion methods for Gabor jet-based face verification'. INST ENGINEERING TECHNOLOGY-IET IET COMPUTER VISION, 1 (3-4), pp. 101-112.
  • Kim T-K, Kittler J, Cipolla R. (2007) 'Discriminative learning and recognition of image set classes using canonical correlations'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 29 (6), pp. 1005-1018.
  • Prabhakar S, Kittler J, Maltoni D, O'Gorman L, Tan T. (2007) 'Introduction to the special issue on biometrics: Progress and directions'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 29 (4), pp. 513-516.
  • Ogata T, Christmas W, Kittler J, Tan JK, Ishikawa S. (2007) 'Human activity detection by combining motion descriptors with boosting'. Journal of Information Processing Society of Japan, 48 Article number 3 , pp. 1166-1175.
  • Chan C-H, Kittler J, Messer K. (2007) 'Multispectral local binary pattern histogram for component-based color face verification'. IEEE 2007 FIRST IEEE INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS, Crystal City, VA: , pp. 306-312.
  • Kittler J, Poh N, Fatukasi O, Messer K, Kryszczuk K, Richiardi J, Drygajlo A. (2007) 'Quality dependent fusion of intramodal and multimodal biometric experts'. SPIE-INT SOC OPTICAL ENGINEERING BIOMETRIC TECHNOLOGY FOR HUMAN IDENTIFICATION IV, Orlando, FL: 6539 Article number ARTN 653903
  • Kim T-K, Kittler J. (2006) 'Design and fusion of pose-invariant face-identification experts'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 16 (9), pp. 1096-1106.
  • Fitch AJ, Kadyrov A, Christmas WJ, Kittler J. (2005) 'Fast robust correlation'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON IMAGE PROCESSING, 14 (8), pp. 1063-1073.
  • Kostin A, Kittler J, Christmas WJ. (2005) 'Object recognition by symmetrised graph matching using relaxation labelling with an inhibitory mechanism'. Pattern Recognition Letters, 26 Article number 3 , pp. 381-393.
  • Messer K, Christmas WJ, Jaser E, Kittler J, Levienaise-Obadia B, Koubaroulis D. (2005) 'A unified approach to the generation of semantic cues for sports video annotation'. ELSEVIER SCIENCE BV SIGNAL PROCESSING, 85 (2), pp. 357-383.

Conference papers

  • Barnard M, Wang W, Kittler J, Naqvi SM, Chambers JA. 'A Dictionary Learning Approach to Tracking'. International Conference on Acoustics, Speech and Signal Processing,
  • Huber P, Hu G, Tena R, Mortazavian P, Koppen P, Christmas WJ, R ̈atsch M, Kittler J. (2016) 'A Multiresolution 3D Morphable Face Model and Fitting Framework'. Rome, Italy: 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

    Abstract

    3D Morphable Face Models are a powerful tool in computer vision. They consist of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmark detection and tracking. However, they are not as widely used as 2D methods - the process of building and using a 3D model is much more involved. In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes. The model contains different mesh resolution levels and landmark point annotations as well as metadata for texture remapping. Accompanying the model is a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals. In addition to basic functionality, it contains pose estimation and face frontalisation algorithms. With the tools presented in this paper, we aim to close two gaps. First, by offering different model resolution levels and fast fitting functionality, we enable the use of a 3D Morphable Model in time-critical applications like tracking. Second, the software library makes it easy for the community to adopt the 3D Morphable Face Model in their research, and it offers a public place for collaboration.

  • Hu G, Yang Y, Yi D, Kittler J, Christmas WJ, Li S, Hospedales T. (2015) 'When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition'. Computer Vision Workshop (ICCVW), 2015 IEEE International Conference on, Santiago, Chile: ICCV workshop ChaLearn Looking at People, pp. 384-392.

    Abstract

    Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a ‘good’ architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

  • Huber P, Feng Z, Christmas WJ, Kittler J, Raetsch M. (2015) 'Fitting 3D Morphable Models using Local Features'. Quebec City, Canada: ICIP 2015
    [ Status: Submitted ]
  • Hu G, Chan CH, Yan F, Christmas W, Kittler J. (2014) 'Robust face recognition by an albedo based 3D morphable model'. IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics,

    Abstract

    © 2014 IEEE. Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike the traditional idea of separating the albedo and illumination contributions using a 3DMM, we propose a novel Albedo Based 3D Morphable Model (AB3DMM), which removes the illumination component from the images using illumination normalisation in a preprocessing step. A comparative study of different illumination normalisation methods for this step is conducted on PIE and Multi-PIE databases. The results show that overall performance of our method outperforms state-of-the-art methods.

  • FarajiDavar N, deCampos TE, Kittler J. (2014) 'Transductive Transfer Machine'. Singapore : Preceedings of the Asian Conference on Computer Vision (ACCV),
  • FarajiDavar N, deCampos TE, Kittler J. (2014) 'Adaptive Transductive Transfer Machine'. Nottingham : Preceedings of the British Machine Vision Conference (BMVC),
  • Coppi D, De Campos T, Yan F, Kittler J, Cucchiara R. (2014) 'On detection of novel categories and subcategories of images using incongruence'. ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014, , pp. 337-344.
  • Poschmann P, Huber P, Raetsch M, Kittler J, Boehme H-J. (2014) 'Fusion of tracking techniques to enhance adaptive real-time tracking of arbitrary objects'. ELSEVIER SCIENCE BV 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION, IHCI 2014, Evry, FRANCE: 6th International Conference on Intelligent Human Computer Interaction (IHCI) 39, pp. 162-165.
  • Kilic V, Barnard M, Wang W, Kittler J. (2013) 'Audio constrained particle filter based visual tracking'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 3627-3631.
  • Barnard M, Wang W, Kittler J. (2013) 'Audio head pose estimation using the direct to reverberant speech ratio'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 8056-8060.
  • Campos TED, Khan A, Yan F, Faraji Davar N, Windridge D, Kittler J, Christmas W. (2013) 'A framework for automatic sports video annotation with anomaly detection and transfer learning'. Proceedings of Machine Learning and Cognitive Science, Palma de Mallorca: 3rd EUCogIII Members Conference
  • Hu G, Mortazavian P, Kittler J, Christmas W. (2013) 'A Facial Symmetry Prior for Improved Illumination Fitting of 3D Morphable Model'. IEEE Proceedings of the 6th IAPR International Conference on Biometrics, Univ Autonoma Madrid, Madrid, SPAIN: 6th IAPR International Conference on Biometrics

    Abstract

    3D face reconstruction from a single 2D image can be performed using a 3D Morphable Model (3DMM) in an analysis-by-synthesis approach. However, the reconstruction is an ill-posed problem. The recovery of the illumination characteristics of the 2D input image is particularly difficult because the proportion of the albedo and shading contributions in a pixel intensity is ambiguous. In this paper we propose the use of a facial symmetry constraint, which helps to identify the relative contributions of albedo and shading. The facial symmetry constraint is incorporated in a multi-feature optimisation framework, which realises the fitting process. By virtue of this constraint better illumination parameters can be recovered, and as a result the estimated 3D face shape and surface texture are more accurate. The proposed method is validated on the PIE face database. The experimental results show that the introduction of facial symmetry constraint improves the performance of both, face reconstruction and face recognition.

  • Kilic V, Barnard M, Wang W, Kittler J. (2013) 'Adaptive particle filtering approach to audio-visual tracking'. European Signal Processing Conference,

    Abstract

    Particle filtering has emerged as a useful tool for tracking problems. However, the efficiency and accuracy of the filter usually depend on the number of particles and noise variance used in the estimation and propagation functions for re-allocating these particles at each iteration. Both of these parameters are specified beforehand and are kept fixed in the regular implementation of the filter which makes the tracker unstable in practice. In this paper we are interested in the design of a particle filtering algorithm which is able to adapt the number of particles and noise variance. The new filter, which is based on audio-visual (AV) tracking, uses information from the tracking errors to modify the number of particles and noise variance used. Its performance is compared with a previously proposed audio-visual particle filtering algorithm with a fixed number of particles and an existing adaptive particle filtering algorithm, using the AV 16.3 dataset with single and multi-speaker sequences. Our proposed approach demonstrates good tracking performance with a significantly reduced number of particles. © 2013 EURASIP.

  • Barnard M, Wang W, Kittler J, Naqvi SM, Chambers J. (2013) 'Audio-visual face detection for tracking in a meeting room environment'. Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, , pp. 1222-1227.
  • Zor C, Windeatt T, Kittler J. (2013) 'ECOC Pruning using Accuracy, Diversity and Hamming Distance Information'. IEEE 2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), CYPRUS: 21st Signal Processing and Communications Applications Conference (SIU)
  • Feng ZH, Kittler J, Christmas W, Wu XJ. (2013) 'Feature level multiple model fusion using multilinear subspace analysis with incomplete training set and its application to face image analysis'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7872 LNCS, pp. 73-84.

    Abstract

    In practical applications of pattern recognition and computer vision, the performance of many approaches can be improved by using multiple models. In this paper, we develop a common theoretical framework for multiple model fusion at the feature level using multilinear subspace analysis (also known as tensor algebra). One disadvantage of the multilinear approach is that it is hard to obtain enough training observations for tensor decomposition algorithms. To overcome this difficulty, we adopted the M2SA algorithm to reconstruct the missing entries of the incomplete training tensor. Furthermore, we apply the proposed framework to the problem of face image analysis using Active Appearance Model (AAM) to validate its performance. Evaluations of AAM using the proposed framework are conducted on Multi-PIE face database with promising results. © Springer-Verlag 2013.

  • Snell V, Christmas W, Kittler J. (2012) 'Texture and shape in fluorescence pattern identification for auto-immune disease diagnosis'. Proceedings - International Conference on Pattern Recognition, , pp. 3750-3753.
  • Yan F, kittler J, mikolajczyk K, windridge D. (2012) 'Automatic Annotation of Court Games with Structured Output Learning'. Tsukuba Science City, JAPAN: International Conference on Pattern Recognition (ICPR) 2012
  • Koppen WP, Chan CH, Christmas WJ, Kittler J. (2012) 'An intrinsic coordinate system for 3D face registration'. Proceedings - International Conference on Pattern Recognition, , pp. 2740-2743.
  • Hu G, Chan C-H, Kittler J, Christmas W. (2012) 'Resolution-Aware 3D Morphable Model'. BMVA Press BMVC, , pp. 1-10.
  • Feng ZH, Kittler J, Christmas W, Wu XJ, Pfeiffer S. (2012) 'Automatic face annotation by multilinear AAM with Missing Values'. Proceedings - International Conference on Pattern Recognition, , pp. 2586-2589.
  • Hu G, Chan CH, Kittler J, Christmas W. (2012) 'Resolution-Aware 3D Morphable Model'. BMVA Press Proceedings of the British Machine Vision Conference, , pp. 109.1-109.10.
  • Mortazavian P, Kittler J, Christmas WJ. (2012) '3D Morphable Model Fitting For Low-Resolution Facial Images'. New Delhi, India: 5TH IAPR International Conference on Biometrics
  • Almajai I, Yan F, de Campos T, Khan A, Christmas W, Windridge D, Kittler J. (2012) 'Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation'. Springer Proceedings of DIRAC Workshop on Detection and Identification of Rare Audivisual Cues, Barcelona, Spain: DIRAC Workshop on Detection and Identification of Rare Audivisual Cues, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 384, pp. 109-117.
  • Snell V, Christmas W, Kittler J. (2012) 'Texture and shape in fluorescence pattern identification for auto-immune disease diagnosis'. IEEE Proceedings - International Conference on Pattern Recognition, Tsukuba: 21st International Conference on Pattern Recognition, pp. 3750-3753.
  • FarajiDavar N, de Campos TE, Windridge D, Kittler J, Christmas W. (2011) 'Domain Adaptation in the Context of Sport Video Action Recognition'. Sierra Nevada, Spain: NIPS 2011 Domain Adaptation Workshop
  • Goswami D, Chan CH, Windridge D, Kittler J. (2011) 'Evaluation of face recognition system in heterogeneous environments (visible vs NIR)'. Proceedings of the IEEE International Conference on Computer Vision, , pp. 2160-2167.

    Abstract

    Performing facial recognition between Near Infrared (NIR) and visible-light (VIS) images has been established as a common method of countering illumination variation problems in face recognition. In this paper we present a new database to enable the evaluation of cross-spectral face recognition. A series of preprocessing algorithms, followed by Local Binary Pattern Histogram (LBPH) representation and combinations with Linear Discriminant Analysis (LDA) are used for recognition. These experiments are conducted on both NIR→VIS and the less common VIS→NIR protocols, with permutations of uni-modal training sets. 12 individual baseline algorithms are presented. In addition, the best performing fusion approaches involving a subset of 12 algorithms are also described. © 2011 IEEE.

  • Awais M, Yan F, Mikolajczyk K, Kittler J. (2011) 'Novel fusion methods for pattern recognition'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6911 LNAI (PART 1), pp. 140-155.

    Abstract

    Over the last few years, several approaches have been proposed for information fusion including different variants of classifier level fusion (ensemble methods), stacking and multiple kernel learning (MKL). MKL has become a preferred choice for information fusion in object recognition. However, in the case of highly discriminative and complementary feature channels, it does not significantly improve upon its trivial baseline which averages the kernels. Alternative ways are stacking and classifier level fusion (CLF) which rely on a two phase approach. There is a significant amount of work on linear programming formulations of ensemble methods particularly in the case of binary classification. In this paper we propose a multiclass extension of binary ν-LPBoost, which learns the contribution of each class in each feature channel. The existing approaches of classifier fusion promote sparse features combinations, due to regularization based on ℓ1-norm, and lead to a selection of a subset of feature channels, which is not good in the case of informative channels. Therefore, we generalize existing classifier fusion formulations to arbitrary ℓp-norm for binary and multiclass problems which results in more effective use of complementary information. We also extended stacking for both binary and multiclass datasets. We present an extensive evaluation of the fusion methods on four datasets involving kernels that are all informative and achieve state-of-the-art results on all of them. © 2011 Springer-Verlag.

  • Huang Q, Cox S, Yan F, deCampos TE, Windridge D, Kittler J, Christmas W. (2011) 'Improved Detection of Ball Hit Events in a Tennis Game Using Multimodal Information'. Volterra, Italy : KTH Computer Science and Communication 11th International Conference on Auditory-Visual Speech Processing (AVSP), Volterra, Italy: International Conference on Auditory-Visual Speech Processing

    Abstract

    We describe a novel framework to detect ball hits in a tennis game by combining audio and visual information. Ball hit detection is a key step in understanding a game such as tennis, but single-mode approaches are not very successful: audio detection suffers from interfering noise and acoustic mismatch, video detection is made difficult by the small size of the ball and the complex background of the surrounding environment. Our goal in this paper is to improve detection performance by focusing on high-level information (rather than low-level features), including the detected audio events, the ball’s trajectory, and inter-event timing information. Visual information supplies coarse detection of the ball-hits events. This information is used as a constraint for audio detection. In addition, useful gains in detection performance can be obtained by using and inter-ballhit timing information, which aids prediction of the next ball hit. This method seems to be very effective in reducing the interference present in low-level features. After applying this method to a women’s doubles tennis game, we obtained improvements in the F-score of about 30% (absolute) for audio detection and about 10% for video detection.

  • Awais M, Yan F, Mikolajczyk K, Kittler J. (2011) 'Augmented Kernel Matrix vs Classifier Fusion for Object Recognition'. BMVA Press Proceedings of the British Machine Vision Conference, Dundee: 22nd British Machine Vision Conference, pp. 60.1-60.11.
  • Awais M, Yan F, Mikolajczyk K, Kittler J. (2011) 'Two-stage augmented kernel matrix for object recognition'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6713 LNCS, pp. 137-146.

    Abstract

    Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recognition problem. Aim of MKL is to learn optimal combination of kernels formed from different features, thus, to learn importance of different feature spaces for classification. Augmented Kernel Matrix (AKM) has recently been proposed to accommodate for the fact that a single training example may have different importance in different feature spaces, in contrast to MKL that assigns same weight to all examples in one feature space. However, AKM approach is limited to small datasets due to its memory requirements. We propose a novel two stage technique to make AKM applicable to large data problems. In first stage various kernels are combined into different groups automatically using kernel alignment. Next, most influential training examples are identified within each group and used to construct an AKM of significantly reduced size. This reduced size AKM leads to same results as the original AKM. We demonstrate that proposed two stage approach is memory efficient and leads to better performance than original AKM and is robust to noise. Results are compared with other state-of-the art MKL techniques, and show improvement on challenging object recognition benchmarks. © 2011 Springer-Verlag.

  • De Campos T, Barnard M, Mikolajczyk K, Kittler J, Yan F, Christmas W, Windridge D. (2011) 'An evaluation of bags-of-words and spatio-temporal shapes for action recognition'. 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, , pp. 344-351.

    Abstract

    Bags-of-visual-Words (BoW) and Spatio-Temporal Shapes (STS) are two very popular approaches for action recognition from video. The former (BoW) is an un-structured global representation of videos which is built using a large set of local features. The latter (STS) uses a single feature located on a region of interest (where the actor is) in the video. Despite the popularity of these methods, no comparison between them has been done. Also, given that BoW and STS differ intrinsically in terms of context inclusion and globality/locality of operation, an appropriate evaluation framework has to be designed carefully. This paper compares these two approaches using four different datasets with varied degree of space-time specificity of the actions and varied relevance of the contextual background. We use the same local feature extraction method and the same classifier for both approaches. Further to BoW and STS, we also evaluated novel variations of BoW constrained in time or space. We observe that the STS approach leads to better results in all datasets whose background is of little relevance to action classification. © 2010 IEEE.

  • Goswami B, Chan CH, Kittler J, Christmas B. (2011) 'Speaker Authentication using Video-based Lip information'. ICASSP,
  • Snell V, Kittler J, Christmas W. (2011) 'Segmentation and Shape Classification of Nuclei in DAPI Images'. British Machine Vision Conference Workshop, University of Dundee: The 22nd British Machine Vision Conference

    Abstract

    This paper addresses issues of analysis of DAPI-stained microscopy images of cell samples, particularly classification of objects as single nuclei, nuclei clusters or nonnuclear material. First, segmentation is significantly improved compared to Otsu’s method[5] by choosing a more appropriate threshold, using a cost-function that explicitly relates to the quality of resulting boundary, rather than image histogram. This method applies ideas from active contour models to threshold-based segmentation, combining the local image sensitivity of the former with the simplicity and lower computational complexity of the latter. Secondly, we evaluate some novel measurements that are useful in classification of resulting shapes. Particularly, analysis of central distance profiles provides a method for improved detection of notches in nuclei clusters. Error rates are reduced to less than half compared to those of the base system, which used Fourier shape descriptors alone.

  • Faraji Davar N, deCampos TE, Kittler J, Yan F. (2011) 'Transductive Transfer Learning for Action Recognition in Tennis Games'. 3rd International Workshop on Video Event Categorization, Tagging and Retrieval for Real-World Applications (VECTaR), in conjunction with ICCV,
  • Yan F, Mikolajczyk K, Kittler J. (2011) 'Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval'. International Workshop on Multiple Classifier Systems
  • Faraji Davar N, de Campos T, Windridge D, Kittler J, Christmas W. (2011) 'Domain Adaptation in the Context of Sport Video Action Recognition'. Domain Adaptation Workshop, in conjunction with NIPS, Sierra Nevada, Spain: NIPS 2011 Domain Adaptation Workshop

    Abstract

    We apply domain adaptation to the problem of recognizing common actions between differing court-game sport videos (in particular tennis and badminton games). Actions are characterized in terms of HOG3D features extracted at the bounding box of each detected player, and thus have large intrinsic dimensionality. The techniques evaluated here for domain adaptation are based on estimating linear transformations to adapt the source domain features in order to maximize the similarity between posterior PDFs for each class in the source domain and the expected posterior PDF for each class in the target domain. As such, the problem scales linearly with feature dimensionality, making the video-environment domain adaptation problem tractable on reasonable time scales and resilient to over-fitting. We thus demonstrate that significant performance improvement can be achieved by applying domain adaptation in this context.

  • Goswami B, Chan C, Kittler J, Christmas W. (2011) 'SPEAKER AUTHENTICATION USING VIDEO-BASED LIP INFORMATION'. IEEE 2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, Prague Congress Ctr, Prague, CZECH REPUBLIC: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1908-1911.
  • Chan CH, Goswami B, Kittler J, Christmas W. (2011) 'Kernel-based Speaker Verification Using Spatiotemporal Lip Information'. Japan : MVA Organization Proceedings of MVA 2011 - IAPR Conference on Machine Vision Applications, Nara, Japan: IAPR Conference on Machine Vision Applications, pp. 422-425.
  • Almajai I, Kittler J, De Campos T, Christmas W, Yan F, Windridge D, Khan A. (2010) 'BALL EVENT RECOGNITION USING HMM FOR AUTOMATIC TENNIS ANNOTATION'. Proceedings of Intl. Conf. on Image Proc.,
  • Chan CH, Goswami B, Kittler J, Christmas W. (2010) 'Local Ordinal Contrast Pattern Histograms for Spatiotemporal, Lip-Based Speaker Authentication'. IEEE Transactions on Information, Forensics and Security, 7, pp. 602-612.
  • Taya S, Windridge D, Kittler J, Osman M. (2010) 'Rule-based modulation of visual attention allocation'. PION LTD PERCEPTION, 39, pp. 81-81.
  • Shaukat A, Windridge D, Hollnagel E, Macchi L, Kittler J. (2010) 'Adaptive, Perception-Action-based Cognitive Modelling of Human Driving Behaviour using Control, Gaze and Signal inputs'. Madrid, Spain: Brain Inspired Cognitive Systems
  • Shaukat A, Windridge D, Hollnagel E, Macchi L, Kittler J. (2010) 'Induction of the Human Perception-Action Hierarchy Employed in Junction-Navigation Scenarios'. Zurich, Switzerland: International Conference on Cognitive Systems, CogSys
  • Chan CH, Kittler J. (2010) 'SPARSE REPRESENTATION OF (MULTISCALE) HISTOGRAMS FOR FACE RECOGNITION ROBUST TO REGISTRATION AND ILLUMINATION PROBLEMS'. IEEE 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, Hong Kong, PEOPLES R CHINA: IEEE International Conference on Image Processing, pp. 2441-2444.
  • Tahir MA, Kittler J, Mikolajczyk K, Yan F. (2010) 'Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers'. SPRINGER-VERLAG BERLIN MULTIPLE CLASSIFIER SYSTEMS, PROCEEDINGS, Cairo, EGYPT: 9th International Workshop on Multiple Classifier Systems 5997, pp. 11-21.
  • Arashloo SR, Kittler J, Christmas WJ. (2010) 'Facial feature localization using graph matching with higher order statistical shape priors and global optimization'. IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010,
  • Yan F, Mikolajczyk K, Kittler J, Tahir MA. (2010) 'Combining Multiple Kernels by Augmenting the Kernel Matrix'. SPRINGER-VERLAG BERLIN MULTIPLE CLASSIFIER SYSTEMS, PROCEEDINGS, Cairo, EGYPT: 9th International Workshop on Multiple Classifier Systems 5997, pp. 175-184.
  • Chan C-H, Kittler J, Tahir MA. (2010) 'Kernel Fusion of Multiple Histogram Descriptors for Robust Face Recognition'. SPRINGER-VERLAG BERLIN STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, Izmir, TURKEY: Joint IAPR International Workshop on SSPR & SPR 6218, pp. 718-727.
  • Almajai I, Yan F, de Campos TE, Khan A, Christmas W, Windridge D, Kittler J. (2010) 'Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation'. Proceedings of DIRAC Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010),
  • Khan A, Windridge D, De Campos T, Kittler J, Christmas W. (2010) 'Lattice-based anomaly rectification for sport video annotation'. Proceedings - International Conference on Pattern Recognition, , pp. 4372-4375.
  • Yan F, Mikolajczyk K, Barnard M, Cai H, Kittler J. (2010) 'Lp Norm Multiple Kernel Fisher Discriminant Analysis for Object and Image Categorisation'. IEEE Conference on Computer Vision and Pattern Recognition
  • Yan F, Kittler J, Mikolajczyk K, Tahir A. (2009) 'Non-sparse multiple kernel learning for fisher discriminant analysis'. Proceedings - IEEE International Conference on Data Mining, ICDM, , pp. 1064-1069.

    Abstract

    We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose an l1 norm regularisation on the kernel weights, which produces sparse solution but may lead to loss of information. In this paper, we propose to use l2 norm regularisation instead. The resulting learning problem is formulated as a semi-infinite program and can be solved efficiently. Through experiments on both synthetic data and a very challenging object recognition benchmark, the relative advantages of the proposed method and its l1 counterpart are demonstrated, and insights are gained as to how the choice of regularisation norm should be made. © 2009 IEEE.

  • Mortazavian P, Kittler J, Christmas W. (2009) 'A 3-D assisted generative model for facial texture super-resolution'. IEEE 2009 IEEE 3RD INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS, Washington, DC: 3rd IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 2009), pp. 452-458.
  • Goswami B, Christmas W, Kittler J. (2009) 'Robust Statistical Estimation Applied to Automatic Lip Segmentation'. SPRINGER-VERLAG BERLIN PATTERN RECOGNITION AND IMAGE ANALYSIS, PROCEEDINGS, Povoa de Varzim, PORTUGAL: 4th Iberian Conference on Pattern Recognition and Image Analysis 5524, pp. 200-207.
  • Poh N, Chan CH, Kittler J, Marcel S, Mc Cool C, Argones Rua E, Alba Castro JL, Villegas M, Paredes R, Struc V, Pavesic N, Salah AA, Fang H, Costen N. (2009) 'Face Video Competition'. SPRINGER-VERLAG BERLIN ADVANCES IN BIOMETRICS, Comp Vis Lab, Alghero, ITALY: 3rd IAPR/IEEE International Conference on Advances in Biometrics 5558, pp. 715-724.
  • Mortazavian P, Kittler J, Christmas W. (2009) '3D-assisted Facial Texture Super-Resolution'. Proceedings of BMVC 2009,
  • Poh N, Wong R, Kittler J, Roli F. (2009) 'Challenges and Research Directions for Adaptive Biometric Recognition Systems'. SPRINGER-VERLAG BERLIN ADVANCES IN BIOMETRICS, Comp Vis Lab, Alghero, ITALY: 3rd IAPR/IEEE International Conference on Advances in Biometrics 5558, pp. 753-764.
  • Poh N, Merati A, Kittler J. (2009) 'Adaptive Client-Impostor Centric Score Normalization: A Case Study in Fingerprint Verification'. IEEE 2009 IEEE 3RD INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS, Washington, DC: 3rd IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 2009), pp. 245-251.
  • Tahir MA, Kittler J, Yan F, Mikolajczyk K. (2009) 'Kernel Discriminant Analysis using Triangular Kernel for Semantic Scene Classification'. IEEE CBMI: 2009 INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING, Chania, GREECE: International Workshop on Content-Based Multimedia Indexing, pp. 1-6.
  • Arashloo SR, Kittler J. (2009) 'Pose-Invariant Face Matching Using MRF Energy Minimization Framework'. SPRINGER-VERLAG BERLIN ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, Univ Bonn, Bonn, GERMANY: 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition 5681, pp. 56-69.
  • Yan F, Mikolajczyk K, Kittler J, Tahir M. (2009) 'A Comparison of l(1) Norm and l(2) Norm Multiple Kernel SVMs in Image and Video Classification'. IEEE CBMI: 2009 INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING, Chania, GREECE: International Workshop on Content-Based Multimedia Indexing, pp. 7-12.
  • Poh N, Kittler J. (2009) 'A Biometric Menagerie Index for Characterising Template/Model-Specific Variation'. SPRINGER-VERLAG BERLIN ADVANCES IN BIOMETRICS, Comp Vis Lab, Alghero, ITALY: 3rd IAPR/IEEE International Conference on Advances in Biometrics 5558, pp. 816-827.
  • Tahir MA, Kittler J, Mikolajczyk K, Yan F. (2009) 'A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling'. SPRINGER-VERLAG BERLIN MULTIPLE CLASSIFIER SYSTEMS, PROCEEDINGS, Univ Iceland, Reykjavik, ICELAND: 8th International Workshop on Multiple Classifier Systems 5519, pp. 82-91.
  • Poh N, Kittler J. (2008) 'On Using Error Bounds to Optimize Cost-Sensitive Multimodal Biometric uthentication'. IEEE IEEE Proceedings of 19th International Conference on Pattern Recognition, Florida, USA: ICPR 2008, pp. 684-687.
  • Zou X, Wang W, Kittler J. (2008) 'Non-negative Matrix Factorization for Face Illumination Analysis'. Proc. ICA Research Network International Workshop, Liverpool, UK: ICARN 2008, pp. 52-55.
  • Poh N, Kittler J. (2008) 'A METHODOLOGY FOR SEPARATING SHEEP FROM GOATS FOR CONTROLLED ENROLLMENT AND MULTIMODAL FUSION'. IEEE 2008 BIOMETRICS SYMPOSIUM (BSYM), Tampa, FL: 6th Biometrics Symposium (BSYM 2008), pp. 17-22.
  • Fatukasi O, Kittler J, Poh N. (2008) 'Estimation of Missing Values in Multimodal Biometric Fusion'. IEEE 2008 IEEE SECOND INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS (BTAS), Washington, DC: 2nd IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 2008), pp. 117-122.
  • Poh N, Kittler J. (2008) 'A Family of Methods for Quality-based Multimodal Biometric Fusion using Generative Classifiers'. IEEE 2008 10TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION: ICARV 2008, VOLS 1-4, Hanoi, VIETNAM: 10th International Conference on Control, Automation, Robotics and Vision, pp. 1162-1167.
  • Kittler J, Windridge D, Goswami D. (2008) 'Subsurface Scattering Deconvolution for Improved NIR-Visible Facial Image Correlation'. IEEE 2008 8TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2008), VOLS 1 AND 2, Amsterdam, NETHERLANDS: 8th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 889-894.
  • Windridge D, Shevchenko M, Kittler J. (2008) 'An Entropy-Based Approach to the Hierarchical Acquisition of Perception-Action Capabilities'. SPRINGER-VERLAG BERLIN COGNITIVE VISION, Santorini, GREECE: 4th International Cognitive Vision Workshop (ICVW 2008) 5329, pp. 79-92.
  • Zou X, Kittler J, Hamouz M, Tena JR. (2008) 'Robust albedo estimation from face image under unknown illumination'. SPIE-INT SOC OPTICAL ENGINEERING BIOMETRIC TECHNOLOGY FOR HUMAN IDENTIFICATION V, Orlando, FL: Conference on Biometric Technology for Human Identification V 6944
  • Poh N, Kittler J, Smith R, Tena JR. (2007) 'A method for estimating authentication performance over time, with applications to face biometrics'. SPRINGER-VERLAG BERLIN PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, Valparaiso, CHILE: 12th Iberoamerican Congress on Pattern Recognition 4756, pp. 360-369.
  • Poh N, Kittler J. (2007) 'Predicting biometric authentication system performance across different application conditions: A bootstrap enhanced parametric approach'. SPRINGER-VERLAG BERLIN Advances in Biometrics, Proceedings, Seoul, SOUTH KOREA: International Conference on Biometrics 4642, pp. 625-635.
  • Hamouz M, Tena JR, Kittler J, Hilton A, Illingworth J. (2007) '3D assisted face recognition: A survey'. SPRINGER 3D IMAGING FOR SAFETY AND SECURITY, San Diego, CA: Workshop on Advanced 3D Imaging for Safety and Security held in Conjunction with the International Conference on Computer Vision and Pattern Recognition 35, pp. 3-23.
  • Wu X-J, Lu J-P, Yang J-Y, Wang S-T, Kittler J. (2007) 'An extreme case of the generalized optimal discriminant transformation and its application to face recognition'. ELSEVIER SCIENCE BV NEUROCOMPUTING, Hefei, PEOPLES R CHINA: International Conference on Intelligent Computing 70 (4-6), pp. 828-834.
  • Poh N, Heusch G, Kittler J. (2007) 'On combination of face authentication experts by a mixture of quality dependent fusion classifiers'. SPRINGER-VERLAG BERLIN Multiple Classifier Systems, Proceedings, Prague, CZECH REPUBLIC: 7th International Workshop on Multiple Classifier Systems 4472, pp. 344-356.
  • Chan C-H, Kittler J, Messer K. (2007) 'Multi-scale local binary pattern histograms for face recognition'. SPRINGER-VERLAG BERLIN Advances in Biometrics, Proceedings, Seoul, SOUTH KOREA: International Conference on Biometrics 4642, pp. 809-818.
  • Poh N, Kittler J. (2007) 'On the use of log-likelihood ratio based model-specific score normalisation in biometric authentication'. SPRINGER-VERLAG BERLIN Advances in Biometrics, Proceedings, Seoul, SOUTH KOREA: International Conference on Biometrics 4642, pp. 614-624.
  • Fatukasi O, Kittler J, Poh N. (2007) 'Quality controlled multimodal fusion of biometric experts'. SPRINGER-VERLAG BERLIN PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, Valparaiso, CHILE: 12th Iberoamerican Congress on Pattern Recognition 4756, pp. 881-890.
  • Poh N, Kittler J, Bourlai T. (2007) 'Improving biometric device interoperability by likelihood ratio-based quality dependent score normalization'. IEEE 2007 FIRST IEEE INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS, Crystal City, VA: 1st IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 2007), pp. 325-329.
  • Kolonias I, Kittler J, Christmas WJ, Yan F. (2007) 'Improving the accuracy of automatic tennis video annotation by high level grammar'. IEEE COMPUTER SOC 14TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING WORKSHOPS, PROCEEDINGS, Modena, ITALY: 14th International Conference on Image Analysis and Processing, pp. 154-159.
  • Kamarainen J-K, Hamouz M, Kittler J, Paalanen P, Ilonen J, Drobchenko A. (2007) 'Object localisation using generative probability model for spatial constellation and local image features'. IEEE 2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, Rio de Janeiro, BRAZIL: 11th IEEE International Conference on Computer Vision, pp. 2784-2791.
  • Tena JR, Smith RS, Hamouz A, Kittler J, Hilton A, Illingworth J. (2007) '2D face pose normalisation using a 3D morphable model'. IEEE 2007 IEEE CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE, London, ENGLAND: IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 51-56.
  • Bourlai T, Kittler J, Messer K. (2007) 'Smart-card-based face verification system: Empirical optimization of system meta-parameters'. IEEE 41ST ANNUAL IEEE INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY, PROCEEDINGS, Ottawa, CANADA: 41st Annual IEEE International Carnahan Conference on Security Technology, pp. 85-92.
  • Khan A, Christmas W, Kittler J. (2007) 'Lip contour segmentation using kernel methods and level sets'. SPRINGER-VERLAG BERLIN ADVANCES IN VISUAL COMPUTING, PROCEEDINGS, PT 2, Lake Tahoe, NV: 3rd International Symposium on Visual Computing 4842, pp. 86-95.
  • Zou X, Kittler J, Messer K. (2007) 'Motion compensation for face recognition based on active differential imaging'. SPRINGER-VERLAG BERLIN Advances in Biometrics, Proceedings, Seoul, SOUTH KOREA: International Conference on Biometrics 4642, pp. 39-48.
  • Roh MC, Christmas B, Kittler J, Lee SW. (2006) 'Gesture spotting in low-quality video with features based on curvature scale space'. FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 2006, pp. 375-380.
  • Kittler J, Shevchenko M, Windridge D. (2006) 'Visual bootstrapping for unsupervised symbol grounding'. SPRINGER-VERLAG BERLIN ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PROCEEDINGS, Antwerp, BELGIUM: 8th International Conference on Advanced Concepts for Intelligent Vision Systems 4179, pp. 1037-1046.
  • Roh MC, Christmas B, Kittler J, Lee SW. (2006) 'Robust player gesture spotting and recognition in low-resolution sports video'. SPRINGER-VERLAG BERLIN COMPUTER VISION - ECCV 2006, PT 4, PROCEEDINGS, Graz, AUSTRIA: 9th European Conference on Computer Vision (ECCV 2006) 3954, pp. 347-358.
  • Kim TK, Kittler J, Cipolla R. (2006) 'Learning discriminative canonical correlations for object recognition with image sets'. SPRINGER-VERLAG BERLIN COMPUTER VISION - ECCV 2006, PT 3, PROCEEDINGS, Graz, AUSTRIA: 9th European Conference on Computer Vision (ECCV 2006) 3953, pp. 251-262.
  • Zou X, Kittler J, Messer K. (2006) 'Accurate face localisation for faces under active near-IR illumination'. IEEE COMPUTER SOC Proceedings of the Seventh International Conference on Automatic Face and Gesture Recognition - Proceedings of the Seventh International Conference, British Mach Vis Assoc, Southampton, ENGLAND: 7th International Conference on Automatic Face and Gesture Recognition, pp. 369-374.
  • Llano EG, Kittler J, Messer K, Vazquez HM. (2006) 'A comparative study of face representations in the frequency domain'. SPRINGER-VERLAG BERLIN PROGRESS IN PATTERN RECOGNITON, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, Cancun, MEXICO: 11th Iberoamerican Conference in Pattern Recognition 4225, pp. 99-108.
  • Guillemaut J-Y, Kittler J, Sadeghi MT, Christmas WJ. (2006) 'General pose face recognition using frontal face model'. SPRINGER-VERLAG BERLIN PROGRESS IN PATTERN RECOGNITON, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, Cancun, MEXICO: 11th Iberoamerican Conference in Pattern Recognition 4225, pp. 79-88.
  • Sanchez UR, Kittler J. (2006) 'Fusion of talking face biometric modalities for personal identity verification'. IEEE 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13, Toulouse, FRANCE: 31st IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5931-5934.
  • Yan F, Kostin A, Christmas W, Kittler J. (2006) 'A novel data association algorithm for object tracking in clutter with application to tennis video analysis'. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 634-641.

    Abstract

    It is well recognised that data association is critically important for object tracking. However, in the presence of successive misdetections, a large number of false candidates and an unknown number of abrupt model switchings that happen unpredictably, the data association problem can be very difficult. We tackle these difficulties by using a layered data association scheme. At the object level, trajectories are "grown" from sets of object candidates that have high probabilities of containing only true positives; by this means the otherwise combinatorial complexity is significantly reduced. Dijkstra 's shortest path algorithm is then used to perform data association at the trajectory level. The algorithm is applied to low-quality tennis video sequences to track a tennis ball. Experiments show that the algorithm is robust to abrupt model switchings, and performs well in heavily cluttered environments. © 2006 IEEE.

  • Zou X, Kittler J, Messer K. (2006) 'Ambient illumination variation removal by active near-IR imaging'. SPRINGER-VERLAG BERLIN ADVANCES IN BIOMETRICS, PROCEEDINGS, Hong Kong, PEOPLES R CHINA: International Conference on Biometrics 3832, pp. 19-25.
  • Sanchez UR, Kittler J. (2006) 'Fusion of talking face biometric modalities for personal identity verification'. IEEE 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol V, Proceedings, Toulouse, FRANCE: 31st IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1073-1076.
  • Rua EA, Kittler J, Castro JLA, Jimenez DG. (2006) 'Information fusion for local Gabor features based frontal face verification'. SPRINGER-VERLAG BERLIN ADVANCES IN BIOMETRICS, PROCEEDINGS, Hong Kong, PEOPLES R CHINA: International Conference on Biometrics 3832, pp. 173-181.
  • Kittler J, Shevchenko M, Windridge D. (2006) 'Cognitive learning with automatic goal acquisition'. I O S PRESS STAIRS 2006, Riva del Garda, ITALY: 3rd Starting Artificial Intelligence Researchers Symposium 142, pp. 3-13.
  • Ogata T, Christmas W, Kittler J, Ishikawa S. (2006) 'Improving human activity detection by combining multi-dimensional motion descriptors with boosting'. Proceedings - International Conference on Pattern Recognition, 1, pp. 295-298.
  • Yan F, Christmas W, Kittler J. (2006) 'A maximum a posteriori probability Viterbi data association algorithm for ball tracking in sports video'. IEEE COMPUTER SOC 18th International Conference on Pattern Recognition, Vol 1, Proceedings, Hong Kong, PEOPLES R CHINA: 18th International Conference on Pattern Recognition (ICPR 2006), pp. 279-282.
  • Hamouz M, Tena JR, Kittler J, Hilton A, Illingworth J. (2006) 'Algorithms for 3D-assisted face recognition'. IEEE 2006 IEEE 14TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1 AND 2, Antalya, TURKEY: IEEE 14th Signal Processing and Communications Applications, pp. 826-829.
  • Goswami B, Christmas WJ, Kittler J. (2006) 'Statistical estimators for use in automatic lip segmentation'. IET Conference Publications, (516 CP), pp. 79-86.
  • Short J, Kittler J, Messer K. (2006) 'Photometric normalisation for component-based face verification'. IEEE COMPUTER SOC Proceedings of the Seventh International Conference on Automatic Face and Gesture Recognition - Proceedings of the Seventh International Conference, British Mach Vis Assoc, Southampton, ENGLAND: 7th International Conference on Automatic Face and Gesture Recognition, pp. 114-119.
  • Bourlai T, Kittler J, Messer K. (2006) 'Database size effects on performance on a smart card face verification system'. IEEE COMPUTER SOC Proceedings of the Seventh International Conference on Automatic Face and Gesture Recognition - Proceedings of the Seventh International Conference, British Mach Vis Assoc, Southampton, ENGLAND: 7th International Conference on Automatic Face and Gesture Recognition, pp. 61-66.
  • Smith RS, Kittler J, Hamouz M, Illingworth J. (2006) 'Face recognition using angular LDA and SVM ensembles'. IEEE COMPUTER SOC 18th International Conference on Pattern Recognition, Vol 3, Proceedings, Hong Kong, PEOPLES R CHINA: 18th International Conference on Pattern Recognition (ICPR 2006), pp. 1008-1012.
  • Sadeghi MT, Kittler J. (2006) 'Confidence based gating of multiple face authentication experts'. SPRINGER-VERLAG BERLIN STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, PROCEEDINGS, Hong Kong, PEOPLES R CHINA: Joint International Workshop on Structural, Syntactic, and Statistical Pattern Recognition 4109, pp. 667-676.
  • Messer K, Kittler J, Short J, Heusch G, Cardinaux F, Marcel S, Rodriguez Y, Shan S, Su Y, Gao W, Chen X. (2006) 'Performance characterisation of face recognition algorithms and their sensitivity to severe illumination changes'. SPRINGER-VERLAG BERLIN ADVANCES IN BIOMETRICS, PROCEEDINGS, Hong Kong, PEOPLES R CHINA: International Conference on Biometrics 3832, pp. 1-11.
  • Christmas WJ, Kostin A, Yan F, Kolonias I, Kittler J. (2005) 'A system for the automatic annotation of tennis matches'. Riga: Fourth International Workshop on Content-Based Multimedia Indexing
  • Kittler J, Christmas WJ, Kostin A, Yan F, Kolonias I, Windridge D. (2005) 'A memory architecture and contextual reasoning framework for cognitive vision'. SPRINGER-VERLAG BERLIN IMAGE ANALYSIS, PROCEEDINGS, Joensuu, FINLAND: 14th Scandinavian Conference on Image Analysis 3540, pp. 343-358.
  • Kittler J, Hamouz M, Tena JR, Hilton A, Illingworth J, Ruiz M. (2005) '3D assisted 2D face recognition: Methodology'. SPRINGER-VERLAG BERLIN PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, Havana, CUBA: 10th Iberoamerican Congress on Pattern Recognition 3773, pp. 1055-1065.
  • Kittler J, Ghaderi R, Windeatt T, Matas J. (2001) 'Face identification and verification via ECOC'. SPRINGER-VERLAG BERLIN AUDIO- AND VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS, Halmstad, SWEDEN: 3rd International Conference on Audio- and Video- Based Biometric Person Authentication 2091, pp. 1-13.
  • Rambaruth R, Christmas W, Kittler J. (2000) 'Representation of regions for accurate motion compensation'. European Signal Processing Conference, 2015-March (March)

Book chapters

  • Yan F, Christmas W, Kittler J. (2014) 'Ball tracking for tennis video annotation'. 71, pp. 25-45.
  • Almajai I, Yan F, de Campos TE, Khan A, Christmas W, Windridge D, Kittler J. (2012) 'Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation'. in Weinshall D, Anemüller J, van Gool L (eds.) Detection and Identification of Rare Audiovisual Cues Springer 384, pp. 109-117.
  • Zou X, Kittler J, Messer K. (2007) 'Illumination invariant face recognition: A survey'. IEEE , pp. 113-120.

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