@conference {14831,
	title = {Wide-baseline stereo for face recognition with large pose variation},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {537 - 544},
	abstract = {2-D face recognition in the presence of large pose variations presents a significant challenge. When comparing a frontal image of a face to a near profile image, one must cope with large occlusions, non-linear correspondences, and significant changes in appearance due to viewpoint. Stereo matching has been used to handle these problems, but performance of this approach degrades with large pose changes. We show that some of this difficulty is due to the effect that foreshortening of slanted surfaces has on window-based matching methods, which are needed to provide robustness to lighting change. We address this problem by designing a new, dynamic programming stereo algorithm that accounts for surface slant. We show that on the CMU PIE dataset this method results in significant improvements in recognition performance.},
	keywords = {2D, algorithm;frontal, dataset;dynamic, estimation;stereo, Face, image, image;near, image;pose, MATCHING, matching;pose, matching;surface, method;dynamic, performance;stereo, PIE, processing;, profile, Programming, programming;face, recognition;CMU, recognition;image, slant;wide-baseline, stereo, stereo;window-based, variation;recognition},
	doi = {10.1109/CVPR.2011.5995559},
	author = {Castillo,C. D and Jacobs, David W.}
}
@conference {14846,
	title = {Using Stereo Matching for 2-D Face Recognition Across Pose},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {We propose using stereo matching for 2-D face recognition across pose. We match one 2-D query image to one 2-D gallery image without performing 3-D reconstruction. Then the cost of this matching is used to evaluate the similarity of the two images. We show that this cost is robust to pose variations. To illustrate this idea we built a face recognition system on top of a dynamic programming stereo matching algorithm. The method works well even when the epipolar lines we use do not exactly fit the viewpoints. We have tested our approach on the PIE dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.},
	keywords = {2D, estimation;stereo, Face, gallery, image, image;2D, image;dynamic, matching;dynamic, matching;pose, processing;, programming;face, programming;pose, query, recognition;2D, recognition;image, variation;stereo},
	doi = {10.1109/CVPR.2007.383111},
	author = {Castillo,C. D and Jacobs, David W.}
}
