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

Facial feature localization using highly flexible yet sufficiently strict shape models

textAccurate and efficient localization of facial features is a crucial first step in many face-related computer vision tasks. Some of these tasks include, but not limited to: identity recognition, expression recognition, and head-pose estimation. Most effort in the field has been exerted towards developing better ways of modeling prior appearance knowledge and image observations. Modeling prior shape knowledge, on the other hand, has not been explored as much. In this dissertation I primarily focus on the limitations of the existing methods in terms of modeling the prior shape knowledge. I first introduce a new pose-constrained shape model. I describe my shape model as being "highly flexible yet sufficiently strict". Existing pose-constrained shape models are either too strict, and have questionable generalization power, or they are too loose, and have questionable localization accuracies. My model tries to find a good middle-ground by learning which shape constraints are more "informative" and should be kept, and which ones are not-so-important and may be omitted. I build my pose-constrained facial feature localization approach on this new shape model using a probabilistic graphical model framework. Within this framework, observed and unobserved variables are defined as the local image observations, and the feature locations, respectively. Feature localization, or "probabilistic inference", is then achieved by nonparametric belief propagation. I show that this approach outperforms other popular pose-constrained methods through qualitative and quantitative experiments. Next, I expand my pose-constrained localization approach to unconstrained setting using a multi-model strategy. While doing so, once again I identify and address the two key limitations of existing multi-model methods: 1) semantically and manually defining the models or "guiding" their generation, and 2) not having efficient and effective model selection strategies. First, I introduce an approach based on unsupervised clustering where the models are automatically learned from training data. Then, I complement this approach with an efficient and effective model selection strategy, which is based on a multi-class naive Bayesian classifier. This way, my method can have many more models, each with a higher level of expressive power, and consequently, provides a more effective partitioning of the face image space. This approach is validated through extensive experiments and comparisons with state-of-the-art methods on state-of-the-art datasets. In the last part of this dissertation I discuss a particular application of the previously introduced techniques; facial feature localization in unconstrained videos. I improve the frame-by-frame localization results, by estimating the actual head-movement from a sequence of noisy head-pose estimates, and then using this information for detecting and fixing the localization failures.Electrical and Computer Engineerin

Robust correlated and individual component analysis

© 1979-2012 IEEE.Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) thetemporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methodson these application domains, outperforming other state-of-the-art methods in the field

Modelling of Orthogonal Craniofacial Profiles

We present a fully-automatic image processing pipeline to build a set of 2D morphable models of three craniofacial profiles from orthogonal viewpoints, side view, front view and top view, using a set of 3D head surface images. Subjects in this dataset wear a close-fitting latex cap to reveal the overall skull shape. Texture-based 3D pose normalization and facial landmarking are applied to extract the profiles from 3D raw scans. Fully-automatic profile annotation, subdivision and registration methods are used to establish dense correspondence among sagittal profiles. The collection of sagittal profiles in dense correspondence are scaled and aligned using Generalised Procrustes Analysis (GPA), before applying principal component analysis to generate a morphable model. Additionally, we propose a new alternative alignment called the Ellipse Centre Nasion (ECN) method. Our model is used in a case study of craniosynostosis intervention outcome evaluation, and the evaluation reveals that the proposed model achieves state-of-the-art results. We make publicly available both the morphable models and the profile dataset used to construct it