Pose manifold curvature is typically less near frontal face views
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Local binary patterns for multi-view facial expression recognition
Computer Vision and Image Understanding
Selecting the best faces to index presentation videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multi-view facial expression recognition analysis with generic sparse coding feature
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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Researchers in psychology have well studied the impact of the pose of a face as perceived by humans, and concluded that the so-called 3/4 view, halfway between the front view and the profile view, is the easiest for face recognition by humans. For face recognition by machines, while much work has been done to create recognition algorithms that are robust to pose variation, little has been done in finding the most representative pose for recognition. In this paper, we use a number of algorithms to evaluate face recognition performance when various poses are used for training. The result, similar to findings in psychology that the 3/4 view is the best, is also justified by the discrimination power of different regions on the face, computed from both the appearance and the geometry of these regions. We believe our study is both scientifically interesting and practically beneficial for many applications.