Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation, emotion recognition, and facial key-point or landmark prediction using learned identity invariant representations. Established approaches to these problems can work well if sufficient examples and labels for a particular identity are available and factors of variation are highly controlled. However, labeled examples of facial expressions, emotions and key-points for new individuals are difficult and costly to obtain. In this paper we improve the ability of techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression. We show how probabilistic modeling of these sources of variation allows one to learn identity-invariant representations for expressions which can then be used to identity-normalize various procedures for facial expression analysis and animation control. We also show how to extend the widely used techniques of active appearance models and constrained local models through replacing the underlying point distribution models which are typically constructed using principal component analysis with identity-expression factorized representations. We present a wide variety of experiments in which we consistently improve performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS

The role of spatial frequency information for ERP components sensitive to faces and emotional facial expression

To investigate the impact of spatial frequency on emotional facial expression analysis, ERPs were recorded in response to low spatial frequency (LSF), high spatial frequency (HSF), and unfiltered broad spatial frequency (BSF) faces with fearful or neutral expressions, houses, and chairs. In line with previous findings, BSF fearful facial expressions elicited a greater frontal positivity than BSF neutral facial expressions, starting at about 150 ms after stimulus onset. In contrast, this emotional expression effect was absent for HSF and LSF faces. Given that some brain regions involved in emotion processing, such as amygdala and connected structures, are selectively tuned to LSF visual inputs, these data suggest that ERP effects of emotional facial expression do not directly reflect activity in these regions. It is argued that higher order neocortical brain systems are involved in the generation of emotion-specific waveform modulations. The face-sensitive N170 component was neither affected by emotional facial expression nor by spatial frequency information

Cultural-based visual expression: Emotional analysis of human face via Peking Opera Painted Faces (POPF)

© 2015 The Author(s) Peking Opera as a branch of Chinese traditional cultures and arts has a very distinct colourful facial make-up for all actors in the stage performance. Such make-up is stylised in nonverbal symbolic semantics which all combined together to form the painted faces to describe and symbolise the background, the characteristic and the emotional status of specific roles. A study of Peking Opera Painted Faces (POPF) was taken as an example to see how information and meanings can be effectively expressed through the change of facial expressions based on the facial motion within natural and emotional aspects. The study found that POPF provides exaggerated features of facial motion through images, and the symbolic semantics of POPF provides a high-level expression of human facial information. The study has presented and proved a creative structure of information analysis and expression based on POPF to improve the understanding of human facial motion and emotion

VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition

Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion

A comparison of facial expression properties in five hylobatid species

Little is known about facial communication of lesser apes (family Hylobatidae) and how their facial expressions (and use of) relate to social organization. We investigated facial expressions (defined as combinations of facial movements) in social interactions of mated pairs in five different hylobatid species belonging to three different genera using a recently developed objective coding system, the Facial Action Coding System for hylobatid species (GibbonFACS). We described three important properties of their facial expressions and compared them between genera. First, we compared the rate of facial expressions, which was defined as the number of facial expressions per units of time. Second, we compared their repertoire size, defined as the number of different types of facial expressions used, independent of their frequency. Third, we compared the diversity of expression, defined as the repertoire weighted by the rate of use for each type of facial expression. We observed a higher rate and diversity of facial expression, but no larger repertoire, in Symphalangus (siamangs) compared to Hylobates and Nomascus species. In line with previous research, these results suggest siamangs differ from other hylobatids in certain aspects of their social behavior. To investigate whether differences in facial expressions are linked to hylobatid socio-ecology, we used a Phylogenetic General Least Square (PGLS) regression analysis to correlate those properties with two social factors: group-size and level of monogamy. No relationship between the properties of facial expressions and these socio-ecological factors was found. One explanation could be that facial expressions in hylobatid species are subject to phylogenetic inertia and do not differ sufficiently between species to reveal correlations with factors such as group size and monogamy level. Am. J. Primatol. 76:618-628, 2014