Koppen WP, Chan CH, Christmas WJ, Kittler J (2012) An intrinsic coordinate system for 3D face registration, Proceedings - International Conference on Pattern Recognitionpp. 2740-2743
We present a method to estimate, based on the horizontal symmetry, an intrinsic coordinate system of faces scanned in 3D. We show that this coordinate system provides an excellent basis for subsequent landmark positioning and model-based refinement such as Active Shape Models, outperforming other -explicit- landmark localisation methods including the commonly-used ICP+ASM approach. © 2012 ICPR Org Committee.
The use of non-negative matrix factorisation (NMF) on 2D face images has been shown to result in sparse feature vectors that encode for local patches on the face, and thus provides a statistically justified approach to learning parts from wholes. However successful on 2D images, the method has so far not been extended to 3D images. The main reason
for this is that 3D space is a continuum and so it is not apparent how to represent 3D coordinates in a non-negative fashion. This work compares different non-negative representations for spatial coordinates, and demonstrates that not all non-negative representations are suitable. We analyse the representational properties that make NMF a successful
method to learn sparse 3D facial features. Using our proposed representation, the factorisation results in sparse and interpretable facial features.
Koppen WP, Worring M (2012) Backtracking: Retrospective multi-target tracking., Computer Vision and Image Understanding1169pp. 967-980
Crouch D, Winney B, Koppen Willem, Christmas William, Hutnik K, Day T, Meena D, Boumertit A, Hysi P, Nessa A, Spector T, Kittler Josef, Bodmer W (2018) The genetics of the human face: Identification of
large-effect single gene variants,Proceedings of the National Academy of Sciences115(4)pp. E676-E685
National Academy of Sciences
To discover specific variants with relatively large effects on the
human face, we have devised an approach to identifying facial
features with high heritability. This is based on using twin data to
estimate the additive genetic value of each point on a face, as
provided by a 3D camera system. In addition, we have used the
ethnic difference between East Asian and European faces as a
further source of face genetic variation. We use principal components
(PCs) analysis to provide a fine definition of the surface
features of human faces around the eyes and of the profile, and
chose upper and lower 10% extremes of the most heritable PCs for
looking for genetic associations. Using this strategy for the
analysis of 3D images of 1,832 unique volunteers from the wellcharacterized
People of the British Isles study and 1,567 unique
twin images from the TwinsUK cohort, together with genetic data
for 500,000 SNPs, we have identified three specific genetic variants
with notable effects on facial profiles and eyes.
This paper investigates the evaluation of dense
3D face reconstruction from a single 2D image in the wild.
To this end, we organise a competition that provides a new
benchmark dataset that contains 2000 2D facial images of
135 subjects as well as their 3D ground truth face scans. In
contrast to previous competitions or challenges, the aim of this
new benchmark dataset is to evaluate the accuracy of a 3D
dense face reconstruction algorithm using real, accurate and
high-resolution 3D ground truth face scans. In addition to the
dataset, we provide a standard protocol as well as a Python
script for the evaluation. Last, we report the results obtained
by three state-of-the-art 3D face reconstruction systems on the
new benchmark dataset. The competition is organised along
with the 2018 13th IEEE Conference on Automatic Face &
3D Morphable Face Models (3DMM) have been used in pattern recognition for some time now. They have been applied as a basis for 3D face recognition, as well as in an assistive role for 2D face recognition to perform geometric and photometric normalisation of the input image, or in 2D face recognition system training. The statistical distribution underlying 3DMM is Gaussian. However, the single-Gaussian model seems at odds with reality when we consider different cohorts of data, e.g. Black and Chinese faces. Their means are clearly different. This paper introduces the Gaussian Mixture 3DMM (GM-3DMM) which models the global population as a mixture of Gaussian subpopulations, each with its own mean. The proposed GM-3DMM extends the traditional 3DMM naturally, by adopting a shared covariance structure to mitigate small sample estimation problems associated with data in high dimensional spaces. We construct a GM-3DMM, the training of which involves a multiple cohort dataset, SURREY-JNU, comprising 942 3D face scans of people with mixed backgrounds. Experiments in fitting the GM-3DMM to 2D face images to facilitate their geometric and photometric normalisation for pose and illumination invariant face recognition demonstrate the merits of the proposed mixture of Gaussians 3D face model.
Fitting 3D Morphable Face Models (3DMM) to a 2D face image allows the separation of face shape from skin texture, as well as correction for face expression. However, the recovered 3D face representation is not readily amenable to processing by convolutional neural networks (CNN). We propose a conformal mapping from a 3D mesh to a 2D image, which makes these machine learning tools accessible by 3D face data. Experiments with a CNN based face recognition system designed using the proposed representation have been carried out to validate the advocated approach. The results obtained on standard benchmarking data sets show its promise.