Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently, researchers have begun to investigate face recognition under unconstrained conditions. Unconstrained Face Recognition provides a comprehensive review of this biometric, especially face recognition from video, assembling a collection of novel approaches that are able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms. Unconstrained Face Recognition is structured to meet the needs of a professional audience of researchers and practitioners in industry. This volume is also suitable for advanced-level students in computer science.

This book constitutes the thoroughly refereed conference proceedings of the International Workshop on Face and Facial Expression Recognition from Real World Videos in conjunction with the 22nd International Conference on Pattern Recognition held in Stockholm, Sweden, in August 2014. The 11 revised full papers were carefully reviewed and selected from numerous submissions and cover topics such as Face Recognition, Face Alignment, Facial Expression Recognition and Facial Images.

Nowadays, facial landmark localization in unconstrained environments has attracted increasing attention in computer vision, which is a fundamental step in face recognition, expression recognition, face tracking, editing, face animation, etc. We firstly introduce the problem of facial landmark localization and its relevant canonical and state-of-the-art techniques. Among the existed methods, when facilitating to the facial images under unconstrained environments, they may encounter problems from the large pose variation, partial occlusion, unpredictable illumination, etc. We then separately investigate each of the pose variation and partial occlusion problems. To overcome the shape variation caused by the pose changes, we propose an optimized part mixture model to fast search in the pose manifold and a bi-stage cascaded deformable shape model to refine the local shape variance. For partial occlusion, we propose a consensus of occlusion-specific regressors framework, which resists from the occlusion due to the large amount of regressors and the particularly designed occlusion patterns. Further, we aim at building a unified framework to jointly deal with the pose and occlusion problems. A pose-conditioned hierarchical part based regression method is designed to condition the pose into several pre-defined subspaces and localize the key positions in a hierarchical way, in which the occlusion is detected by the part regressors and further propagated through the hierarchical structure. The proposed facial landmark localization methods have shown more promising performance than those state-of-the-arts in both accuracy and efficiency, compared on both lab-environmental databases and multiple challenging faces-in-the-wild databases. Our face alignment methods are further applied to some human-computer interaction (HCI) applications, i.e. user-defined expression recognition and face and gesture based visual deception detection. The improved results from the applications further validate the advantages of our method under all kinds of uncontrolled conditions.

This book presents the state-of-the-art in face detection and analysis. It outlines new research directions, including in particular psychology-based facial dynamics recognition, aimed at various applications such as behavior analysis, deception detection, and diagnosis of various psychological disorders. Topics of interest include face and facial landmark detection, face recognition, facial expression and emotion analysis, facial dynamics analysis, face classification, identification, and clustering, and gaze direction and head pose estimation, as well as applications of face analysis.

This book aims to bring together selected recent advances, applications and original results in the area of biometric face recognition. They can be useful for researchers, engineers, graduate and postgraduate students, experts in this area and hopefully also for people interested generally in computer science, security, machine learning and artificial intelligence. Various methods, approaches and algorithms for recognition of human faces are used by authors of the chapters of this book, e.g. PCA, LDA, artificial neural networks, wavelets, curvelets, kernel methods, Gabor filters, active appearance models, 2D and 3D representations, optical correlation, hidden Markov models and others. Also a broad range of problems is covered: feature extraction and dimensionality reduction (chapters 1-4), 2D face recognition from the point of view of full system proposal (chapters 5-10), illumination and pose problems (chapters 11-13), eye movement (chapter 14), 3D face recognition (chapters 15-19) and hardware issues (chapters 19-20).