Download or read book Unconstrained Face Landmark Localization written by Xiang Yu and published by . This book was released on 2015 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.