Contextual Identity Recognition in Personal Photo Albums Export

@inproceedings{citeulike:1668197, abstract = {We present an efficient probabilistic method for identity recognition in personal photo albums. Personal photos are usually taken under uncontrolled conditions - the captured faces exhibit significant variations in pose, expression and illumination that limit the success of traditional face recognition algorithms. We show how to improve recognition rates by incorporating additional cues present in personal photo collections, such as clothing appearance and information about when the photo was taken. This is done by constructing a Markov Random Field (MRF) that effectively combines all available contextual cues in a principled recognition framework. Performing inference in the MRF produces markedly improved recognition results in a challenging dataset consisting of the personal photo collections of multiple people. At the same time, the computational cost of our approach remains comparable to that of standard face recognition approaches.}, author = {Anguelov, Dragomir and Lee, Kuang-Chih and Gokturk, Salih B. and Sumengen, Baris}, booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on}, citeulike-article-id = {1668197}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CVPR.2007.383057}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270082}, doi = {10.1109/CVPR.2007.383057}, journal = {Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on}, keywords = {mrf}, pages = {1--7}, posted-at = {2007-09-18 08:08:34}, priority = {5}, title = {Contextual Identity Recognition in Personal Photo Albums}, url = {http://dx.doi.org/10.1109/CVPR.2007.383057}, year = {2007} }

TY - CONF ID - citeulike:1668197 L3 - citeulike-article-id:1668197 N2 - We present an efficient probabilistic method for identity recognition in personal photo albums. Personal photos are usually taken under uncontrolled conditions - the captured faces exhibit significant variations in pose, expression and illumination that limit the success of traditional face recognition algorithms. We show how to improve recognition rates by incorporating additional cues present in personal photo collections, such as clothing appearance and information about when the photo was taken. This is done by constructing a Markov Random Field (MRF) that effectively combines all available contextual cues in a principled recognition framework. Performing inference in the MRF produces markedly improved recognition results in a challenging dataset consisting of the personal photo collections of multiple people. At the same time, the computational cost of our approach remains comparable to that of standard face recognition approaches. JF - Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on EP - 7 TI - Contextual Identity Recognition in Personal Photo Albums T2 - Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on SP - 1 KW - mrf AU - Anguelov, Dragomir AU - Lee, Kuang-Chih AU - Gokturk, Salih AU - Sumengen, Baris PY - 2007/// UR - http://dx.doi.org/10.1109/CVPR.2007.383057 ER -