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Learning patch dependencies for improved pose mismatched face verification
S. Lucey and T. Chen
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2006.

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Most pose robust face verification algorithms, which employ 2D appearance, rely heavily on statistics gathered from offline databases containing ample facial appearance variation across many views. Due to the high dimensionality of the face images being employed, the validity of the assumptions employed in obtaining these statistics are essential for good performance. In this paper we assess three common approaches in 2D appearance pose mismatched face recognition literature. In our experiments we demonstrate where these approaches work and fail. As a result of this analysis, we additionally propose a new algorithm that attempts to learn the statistical dependency between gallery patches (i.e. local regions of pixels) and the whole appearance of the probe image. We demonstrate improved performance over a number of leading 2D appearance face recognition algorithms.

Associated center: VASC
Associated labs/groups: Human Identification at a Distance and Face Group
Associated projects: Facial Expression Analysis and Face Recognition Across Pose

Number of pages: 7

S. Lucey and T. Chen, "Learning patch dependencies for improved pose mismatched face verification," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2006.

@inproceedings{Lucey_2006_5491,
   author = "Simon Lucey and Tsuhan Chen",
   title = "Learning patch dependencies for improved pose mismatched face verification",
   booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2006"
}