Discriminant mutual subspace learning for indoor and outdoor face recognition
Refereed conference paper presented and published in conference proceedings


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摘要Outdoor face recognition is among the most challenging problems for face recognition. In this paper, we develop a discriminant mutual subspace learning algorithm for indoor and outdoor face recognition. Unlike traditional algorithms using one subspace to model both indoor and outdoor face images, our algorithm simultaneously learn two related subspaces for indoor and outdoor images respectively thus can better model both. To further improve the recognition performance we develop a DMSL-based multiclassifier fusion framework on Gabor images using a new fusion method called adaptive informative fusion scheme. Experimental results clearly show that this framework can greatly enhance the recognition performance.
著者Li ZF, Lin DH, Meng H, Tang XO
會議名稱IEEE Conference on Computer Vision and Pattern Recognition
會議開始日17.06.2007
會議完結日22.06.2007
會議地點Minneapolis
會議國家/地區美國
出版年份2007
月份1
日期1
出版社IEEE
頁次1027 - 1032
國際標準書號978-1-4244-1179-5
國際標準期刊號1063-6919
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Software Engineering; Imaging Science & Photographic Technology; Mathematical & Computational Biology; Remote Sensing

上次更新時間 2021-19-01 於 00:55