@conference {14850,
	title = {Trainable 3D recognition using stereo matching},
	booktitle = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011///},
	pages = {625 - 631},
	abstract = {Stereo matching has been used for face recognition in the presence of pose variation. In this approach, stereo matching is used to compare two 2-D images based on correspondences that reflect the effects of viewpoint variation and allow for occlusion. We show how to use stereo matching to derive image descriptors that can be used to train a classifier. This improves face recognition performance, producing the best published results on the CMU PIE dataset. We also demonstrate that classification based on stereo matching can be used for general object classification in the presence of pose variation. In preliminary experiments we show promising results on the 3D object class dataset, a standard, challenging 3D classification data set.},
	keywords = {2D, 3D, class, classification, classification;image, data, dataset;CMU, dataset;face, descriptor;occlusion;pose, estimation;solid, image, image;3D, matching;pose, matching;trainable, modelling;stereo, object, PIE, processing;, recognition;face, recognition;image, set;3D, variation;stereo},
	doi = {10.1109/ICCVW.2011.6130301},
	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.}
}
