Learning patch dependencies for improved pose mismatched face verification

Simon Lucey and Tsuhan Chen
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July, 2006.


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Abstract
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.

Keywords
Face Verification, Pose Mismatch

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Human Identification at a Distance, Face Group, Component Analysis
Associated Project(s): Facial Expression Analysis and Face Recognition Across Pose
Number of pages: 7

Text Reference
Simon Lucey and Tsuhan Chen, "Learning patch dependencies for improved pose mismatched face verification," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July, 2006.

BibTeX Reference
@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 = "July",
   year = "2006",
}