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Pose and illumination are considered as two main challenges that face recognition system encounters. In this paper, we consider face recognition problem across pose and illumination variations, given small amount of training samples and single sample per gallery (a.k.a., one shot classification). We combine the strength of 3D models in generating multiviews and various illumination samples and the...
Noticing that face images(from different persons) with high similarity computed by current state-of-the-art methods may be not visually similar, in this paper, we present a new verification problem on judging whether the given faces are similar or not. Similar to “view 2” of Labeled Faces in the Wild(LFW), we construct ten subsets' face pairs using images from LFW. Label of each pair comes from human...
State-of-the-art methods have reported very high performance on facial expression detection. However, nearly all these previous work was conducted in strictly controlled environment, what's more, effects of imbalanced data have been neglected. A new database, RAF-DB, is constructed to provide abundant images with expression labels from different people in different real-world conditions. Annotation...
Current deep learning methods have achieved human-level performance on Labeled Faces in the Wild (LFW) database, but we think it is because that the limited number of pairs on LFW do not capture the real difficulty of large-scale unconstrained face verification problem. Besides the intra-class variations like pose, illumination, occlusion and expression, highly visually similarity of different persons'...
Identifying subjects with variations caused by poses is one of the most challenging problems in face recognition, essentially, a misalignment problem. In this paper, we propose a simple, practical but effective continuous pose normalization method to handle pose variations. First, 2D-3D correspondence is constructed based on five facial landmarks of query image. A single reference 3D mesh is projected...
Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated...
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