Face Transfer with Multilinear Models

Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities x expressions x visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.Engineering and Applied Science

Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models

Currently there is no complete face recognition system that is invariant to all facial expressions. Although humans find it easy to identify and recognise faces regardless of changes in illumination, pose and expression, producing a computer system with a similar capability has proved to be particularly di cult. Three dimensional face models are geometric in nature and therefore have the advantage of being invariant to head pose and lighting. However they are still susceptible to facial expressions. This can be seen in the decrease in the recognition results using principal component analysis when expressions are added to a data set. In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organised in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognising high intensity expressions that are not in the training datasets. We have determined, experimentally, a set of anatomical landmarks that best describe facial expression e ectively. We found that the best placement of landmarks to distinguish di erent facial expressions are in areas around the prominent features, such as the cheeks and eyebrows. Recognition results using landmark-based face recognition could be improved with better placement. We looked into the possibility of achieving expression-invariant face recognition by reconstructing and manipulating realistic facial expressions. We proposed a tensor-based statistical discriminant analysis method to reconstruct facial expressions and in particular to neutralise facial expressions. The results of the synthesised facial expressions are visually more realistic than facial expressions generated using conventional active shape modelling (ASM). We then used reconstructed neutral faces in the sub-tensor framework for recognition purposes. The recognition results showed slight improvement. Besides biometric recognition, this novel tensor-based synthesis approach could be used in computer games and real-time animation applications

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012

Fine-Scaled 3D Geometry Recovery from Single RGB Images

3D geometry recovery from single RGB images is a highly ill-posed and inherently ambiguous problem, which has been a challenging research topic in computer vision for several decades. When fine-scaled 3D geometry is required, the problem become even more difficult. 3D geometry recovery from single images has the objective of recovering geometric information from a single photograph of an object or a scene with multiple objects. The geometric information that is to be retrieved can be of different representations such as surface meshes, voxels, depth maps or 3D primitives, etc. In this thesis, we investigate fine-scaled 3D geometry recovery from single RGB images for three categories: facial wrinkles, indoor scenes and man-made objects. Since each category has its own particular features, styles and also variations in representation, we propose different strategies to handle different 3D geometry estimates respectively. We present a lightweight non-parametric method to generate wrinkles from monocular Kinect RGB images. The key lightweight feature of the method is that it can generate plausible wrinkles using exemplars from one high quality 3D face model with textures. The local geometric patches from the source could be copied to synthesize different wrinkles on the blendshapes of specific users in an offline stage. During online tracking, facial animations with high quality wrinkle details can be recovered in real-time as a linear combination of these personalized wrinkled blendshapes. We propose a fast-to-train two-streamed CNN with multi-scales, which predicts both dense depth map and depth gradient for single indoor scene images.The depth and depth gradient are then fused together into a more accurate and detailed depth map. We introduce a novel set loss over multiple related images. By regularizing the estimation between a common set of images, the network is less prone to overfitting and achieves better accuracy than competing methods. Fine-scaled 3D point cloud could be produced by re-projection to 3D using the known camera parameters. To handle highly structured man-made objects, we introduce a novel neural network architecture for 3D shape recovering from a single image. We develop a convolutional encoder to map a given image to a compact code. Then an associated recursive decoder maps this code back to a full hierarchy, resulting a set of bounding boxes to represent the estimated shape. Finally, we train a second network to predict the fine-scaled geometry in each bounding box at voxel level. The per-box volumes are then embedded into a global one, and from which we reconstruct the final meshed model. Experiments on a variety of datasets show that our approaches can estimate fine-scaled geometry from single RGB images for each category successfully, and surpass state-of-the-art performance in recovering faithful 3D local details with high resolution mesh surface or point cloud

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Weakly-Labeled Data and Identity-Normalization for Facial Image Analysis

RÉSUMÉ Cette thèse traite de l’amélioration de la reconnaissance faciale et de l’analyse de l’expression du visage en utilisant des sources d’informations faibles. Les données étiquetées sont souvent rares, mais les données non étiquetées contiennent souvent des informations utiles pour l’apprentissage d’un modèle. Cette thèse décrit deux exemples d’utilisation de cette idée. Le premier est une nouvelle méthode pour la reconnaissance faciale basée sur l’exploitation de données étiquetées faiblement ou bruyamment. Les données non étiquetées peuvent être acquises d’une manière qui offre des caractéristiques supplémentaires. Ces caractéristiques, tout en n’étant pas disponibles pour les données étiquetées, peuvent encore être utiles avec un peu de prévoyance. Cette thèse traite de la combinaison d’un ensemble de données étiquetées pour la reconnaissance faciale avec des images des visages extraits de vidéos sur YouTube et des images des visages obtenues à partir d’un moteur de recherche. Le moteur de recherche web et le moteur de recherche vidéo peuvent être considérés comme de classificateurs très faibles alternatifs qui fournissent des étiquettes faibles. En utilisant les résultats de ces deux types de requêtes de recherche comme des formes d’étiquettes faibles différents, une méthode robuste pour la classification peut être développée. Cette méthode est basée sur des modèles graphiques, mais aussi incorporant une marge probabiliste. Plus précisément, en utilisant un modèle inspiré par la variational relevance vector machine (RVM), une alternative probabiliste à la support vector machine (SVM) est développée. Contrairement aux formulations précédentes de la RVM, le choix d’une probabilité a priori exponentielle est introduit pour produire une approximation de la pénalité L1. Les résultats expérimentaux où les étiquettes bruyantes sont simulées, et les deux expériences distinctes où les étiquettes bruyantes de l’image et les résultats de recherche vidéo en utilisant des noms comme les requêtes indiquent que l’information faible dans les étiquettes peut être exploitée avec succès. Puisque le modèle dépend fortement des méthodes noyau de régression clairsemées, ces méthodes sont examinées et discutées en détail. Plusieurs algorithmes différents utilisant les distributions a priori pour encourager les modèles clairsemés sont décrits en détail. Des expériences sont montrées qui illustrent le comportement de chacune de ces distributions. Utilisés en conjonction avec la régression logistique, les effets de chaque distribution sur l’ajustement du modèle et la complexité du modèle sont montrés. Les extensions aux autres méthodes d’apprentissage machine sont directes, car l’approche est ancrée dans la probabilité bayésienne. Une expérience dans la prédiction structurée utilisant un conditional random field pour une tâche d’imagerie médicale est montrée pour illustrer comment ces distributions a priori peuvent être incorporées facilement à d’autres tâches et peuvent donner de meilleurs résultats. Les données étiquetées peuvent également contenir des sources faibles d’informations qui ne peuvent pas nécessairement être utilisées pour un effet maximum. Par exemple les ensembles de données d’images des visages pour les tâches tels que, l’animation faciale contrôlée par les performances des comédiens, la reconnaissance des émotions, et la prédiction des points clés ou les repères du visage contiennent souvent des étiquettes alternatives par rapport à la tâche d’internet principale. Dans les données de reconnaissance des émotions, par exemple, des étiquettes de l’émotion sont souvent rares. C’est peut-être parce que ces images sont extraites d’une vidéo, dans laquelle seul un petit segment représente l’étiquette de l’émotion. En conséquence, de nombreuses images de l’objet sont dans le même contexte en utilisant le même appareil photo ne sont pas utilisés. Toutefois, ces données peuvent être utilisées pour améliorer la capacité des techniques d’apprentissage de généraliser pour des personnes nouvelles et pas encore vues en modélisant explicitement les variations vues précédemment liées à l’identité et à l’expression. Une fois l’identité et de la variation de l’expression sont séparées, les approches supervisées simples peuvent mieux généraliser aux identités de nouveau. Plus précisément, dans cette thèse, la modélisation probabiliste de ces sources de variation est utilisée pour identité normaliser et des diverses représentations d’images faciales. Une variété d’expériences sont décrites dans laquelle la performance est constamment améliorée, incluant la reconnaissance des émotions, les animations faciales contrôlées par des visages des comédiens sans marqueurs et le suivi des points clés sur des visages. Dans de nombreux cas dans des images faciales, des sources d’information supplémentaire peuvent être disponibles qui peuvent être utilisées pour améliorer les tâches d’intérêt. Cela comprend des étiquettes faibles qui sont prévues pendant la collecte des données, telles que la requête de recherche utilisée pour acquérir des données, ainsi que des informations d’identité dans le cas de plusieurs bases de données d’images expérimentales. Cette thèse soutient en principal que cette information doit être utilisée et décrit les méthodes pour le faire en utilisant les outils de la probabilité.----------ABSTRACT This thesis deals with improving facial recognition and facial expression analysis using weak sources of information. Labeled data is often scarce, but unlabeled data often contains information which is helpful to learning a model. This thesis describes two examples of using this insight. The first is a novel method for face-recognition based on leveraging weak or noisily labeled data. Unlabeled data can be acquired in a way which provides additional features. These features, while not being available for the labeled data, may still be useful with some foresight. This thesis discusses combining a labeled facial recognition dataset with face images extracted from videos on YouTube and face images returned from using a search engine. The web search engine and the video search engine can be viewed as very weak alternative classifier which provide “weak labels.” Using the results from these two different types of search queries as forms of weak labels, a robust method for classification can be developed. This method is based on graphical models, but also encorporates a probabilistic margin. More specifically, using a model inspired by the variational relevance vector machine (RVM), a probabilistic alternative to transductive support vector machines (TSVM) is further developed. In contrast to previous formulations of RVMs, the choice of an Exponential hyperprior is introduced to produce an approximation to the L1 penalty. Experimental results where noisy labels are simulated and separate experiments where noisy labels from image and video search results using names as queries both indicate that weak label information can be successfully leveraged. Since the model depends heavily on sparse kernel regression methods, these methods are reviewed and discussed in detail. Several different sparse priors algorithms are described in detail. Experiments are shown which illustrate the behavior of each of these sparse priors. Used in conjunction with logistic regression, each sparsity inducing prior is shown to have varying effects in terms of sparsity and model fit. Extending this to other machine learning methods is straight forward since it is grounded firmly in Bayesian probability. An experiment in structured prediction using Conditional Random Fields on a medical image task is shown to illustrate how sparse priors can easily be incorporated in other tasks, and can yield improved results. Labeled data may also contain weak sources of information that may not necessarily be used to maximum effect. For example, facial image datasets for the tasks of performance driven facial animation, emotion recognition, and facial key-point or landmark prediction often contain alternative labels from the task at hand. In emotion recognition data, for example, emotion labels are often scarce. This may be because these images are extracted from a video, in which only a small segment depicts the emotion label. As a result, many images of the subject in the same setting using the same camera are unused. However, this data can be used to improve the ability of learning techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. Once identity and expression variation are separated, simpler supervised approaches can work quite well to generalize to unseen subjects. More specifically, in this thesis, probabilistic modeling of these sources of variation is used to “identity-normalize” various facial image representations. A variety of experiments are described in which performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking is consistently improved. This includes an algorithm which shows how this kind of normalization can be used for facial key-point localization. In many cases in facial images, sources of information may be available that can be used to improve tasks. This includes weak labels which are provided during data gathering, such as the search query used to acquire data, as well as identity information in the case of many experimental image databases. This thesis argues in main that this information should be used and describes methods for doing so using the tools of probability

Inferring Human Pose and Motion from Images

As optical gesture recognition technology advances, touchless human computer interfaces of the future will soon become a reality. One particular technology, markerless motion capture, has gained a large amount of attention, with widespread application in diverse disciplines, including medical science, sports analysis, advanced user interfaces, and virtual arts. However, the complexity of human anatomy makes markerless motion capture a non-trivial problem: I) parameterised pose configuration exhibits high dimensionality, and II) there is considerable ambiguity in surjective inverse mapping from observation to pose configuration spaces with a limited number of camera views. These factors together lead to multimodality in high dimensional space, making markerless motion capture an ill-posed problem. This study challenges these difficulties by introducing a new framework. It begins with automatically modelling specific subject template models and calibrating posture at the initial stage. Subsequent tracking is accomplished by embedding naturally-inspired global optimisation into the sequential Bayesian filtering framework. Tracking is enhanced by several robust evaluation improvements. Sparsity of images is managed by compressive evaluation, further accelerating computational efficiency in high dimensional space