Multilinear methods for disentangling variations with applications to facial analysis

Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data. It is assumed that the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich structure of visual object appearance. Disentangling such unobserved factors from visual data is a challenging task, especially when the data have been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label information is not available. The work presented in this thesis focuses on disentangling the variations contained in visual data, in particular applied to 2D and 3D faces. The motivation behind this work lies in recent developments in the field, such as (i) the creation of large, visual databases for face analysis, with (ii) the need of extracting information without the use of labels and (iii) the need to deploy systems under demanding, real-world conditions. In the first part of this thesis, we present a method to synthesise plausible 3D expressions that preserve the identity of a target subject. This method is supervised as the model uses labels, in this case 3D facial meshes of people performing a defined set of facial expressions, to learn. The ability to synthesise an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and computer vision, ranging from the ecient and cost-e↵ective creation of CG characters to scalable data generation for machine learning purposes. Unlike previous methods based on multilinear models, the proposed approach is capable to extrapolate well outside the sample pool, which allows it to accurately reproduce the identity of the target subject and create artefact-free expression shapes while requiring only a small input dataset. We introduce global-local multilinear models that leverage the strengths of expression-specific and identity-specific local models combined with coarse motion estimations from a global model. The expression-specific and identity-specific local models are built from di↵erent slices of the patch-wise local multilinear model. Experimental results show that we achieve high-quality, identity-preserving facial expression synthesis results that outperform existing methods both quantitatively and qualitatively. In the second part of this thesis, we investigate how the modes of variations from visual data can be extracted. Our assumption is that visual data has an underlying structure consisting of factors of variation and their interactions. Finding this structure and the factors is important as it would not only help us to better understand visual data but once obtained we can edit the factors for use in various applications. Shape from Shading and expression transfer are just two of the potential applications. To extract the factors of variation, several supervised methods have been proposed but they require both labels regarding the modes of variations and the same number of samples under all modes of variations. Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. We propose a novel general multilinear matrix decomposition method that discovers the multilinear structure of possibly incomplete sets of visual data in unsupervised setting. We demonstrate the applicability of the proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the wild and with occlusion removal), expression transfer, and estimation of surface normals from images captured in the wild. Finally, leveraging the unsupervised multilinear method proposed as well as recent advances in deep learning, we propose a weakly supervised deep learning method for disentangling multiple latent factors of variation in face images captured in-the-wild. To this end, we propose a deep latent variable model, where we model the multiplicative interactions of multiple latent factors of variation explicitly as a multilinear structure. We demonstrate that the proposed approach indeed learns disentangled representations of facial expressions and pose, which can be used in various applications, including face editing, as well as 3D face reconstruction and classification of facial expression, identity and pose.Open Acces

3D face recognition using photometric stereo

Automatic face recognition has been an active research area for the last four decades. This thesis explores innovative bio-inspired concepts aimed at improved face recognition using surface normals. New directions in salient data representation are explored using data captured via a photometric stereo method from the University of the West of England’s “Photoface” device. Accuracy assessments demonstrate the advantage of the capture format and the synergy offered by near infrared light sources in achieving more accurate results than under conventional visible light. Two 3D face databases have been created as part of the thesis – the publicly available Photoface database which contains 3187 images of 453 subjects and the 3DE-VISIR dataset which contains 363 images of 115 people with different expressions captured simultaneously under near infrared and visible light. The Photoface database is believed to be the ?rst to capture naturalistic 3D face models. Subsets of these databases are then used to show the results of experiments inspired by the human visual system. Experimental results show that optimal recognition rates are achieved using surprisingly low resolution of only 10x10 pixels on surface normal data, which corresponds to the spatial frequency range of optimal human performance. Motivated by the observed increase in recognition speed and accuracy that occurs in humans when faces are caricatured, novel interpretations of caricaturing using outlying data and pixel locations with high variance show that performance remains disproportionately high when up to 90% of the data has been discarded. These direct methods of dimensionality reduction have useful implications for the storage and processing requirements for commercial face recognition systems. The novel variance approach is extended to recognise positive expressions with 90% accuracy which has useful implications for human-computer interaction as well as ensuring that a subject has the correct expression prior to recognition. Furthermore, the subject recognition rate is improved by removing those pixels which encode expression. Finally, preliminary work into feature detection on surface normals by extending Haar-like features is presented which is also shown to be useful for correcting the pose of the head as part of a fully operational device. The system operates with an accuracy of 98.65% at a false acceptance rate of only 0.01 on front facing heads with neutral expressions. The work has shown how new avenues of enquiry inspired by our observation of the human visual system can offer useful advantages towards achieving more robust autonomous computer-based facial recognition

Combined Face-Brain Morphology and Associated Neurocognitive Correlates in Fetal Alcohol Spectrum Disorders

BACKGROUND: Since the 1970s, a range of facial, neurostructural, and neurocognitive adverse effects have been shown to be associated with prenatal alcohol exposure. Typically, these effects are studied individually and not in combination. Our objective is to improve the understanding of the teratogenic effects of prenatal alcohol exposure by simultaneously considering face-brain morphology and neurocognitive measures. METHODS: Participants were categorized as control (n = 47), fetal alcohol syndrome (FAS, n = 22), or heavily exposed (HE) prenatally, but not eligible for a FAS diagnosis (HE, n = 50). Structural brain MRI images and high-resolution 3D facial images were analyzed using dense surface models of features of the face and surface shape of the corpus callosum (CC) and caudate nucleus (CN). Asymmetry of the CN was evaluated for correlations with neurocognitive measures. RESULTS: (i) Facial growth delineations for FAS, HE, and controls are replicated for the CN and the CC. (ii) Concordance of clinical diagnosis and face-based control-FAS discrimination improves when the latter is combined with specific brain regions. In particular, midline facial regions discriminate better when combined with a midsagittal profile of the CC. (iii) A subset of HE individuals was identified with FAS-like CN dysmorphism. The average of this HE subset was FAS-like in its facial dysmorphism. (iv) Right-left asymmetry found in the CNs of controls is not apparent for FAS, is diminished for HE, and correlates with neurocognitive measures in the combined FAS and HE population. CONCLUSIONS: Shape analysis which combines facial regions with the CN, and with the CC, better identify those with FAS. CN asymmetry was reduced for FAS compared to controls and is strongly associated with general cognitive ability, verbal learning, and recall in those with prenatal alcohol exposure. This study further extends the brain-behavior relationships known to be vulnerable to alcohol teratogenesis