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Research Topic: Face Recognition
Face recognition is one of the most nature techniques for identifying human beings. The major challenges of face recognition include various facial variations (e.g., expression, illumination, occlusion, and so on) and typically small sample size (in the extreme case, one sample per person).
I have developed methods to deal with the large facial variations and the small sample size problem.
My published papers on this topic:
Xiaoyang Tan, Yi Li, Jun Liu, and Lin Jiang
European Conference on Computer Vision (ECCV) 2010
How to cope with spoofing with photograph or video in a face recognition system?
Yuliang Zhu, Jun Liu, Songcan Chen
Image & Vision Computing, 27, no. 9 (2009): 1358-1370
We applied the sub-pattern and random subspace technique for face recognition.
Xiaoyang Tan, Songcan Chen, Zhihua Zhou, and Jun Liu
IEEE Transactions on Information Forensics & Security, 4, no.2 (2009), 217-230
We proposed the partial similarity for face recognition.
Jun Liu, Songcan Chen and Xiaoyang Tan
Pattern Recognition 41, no. 1 (2008): 378-395
Face images can be represented as a natural two-dimensional matrix.
How about the influence of the facial expression on the leading singular vectors?
Fractional order Singular Value Decomposition?
Jun Liu, Songcan Chen, Xiaoyang Tan and Daoqiang Zhang
International Journal of Pattern Recognition and Artificial Intelligence, 21, no. 8 (2007): 1265-1278
Jun Liu, Songcan Chen, Zhihua Zhou, and Xiaoyang Tan
IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG, In conjunction with ICCV) 2007
Build a subspace for each image using different filtered images?
Quite good results on well known face data sets such as FERET, AR, and Extended YALE.
Xiaoyang Tan, Jun Liu and Songcan Chen
Neurocomputing 69, no. 13-15 (2006): 1796-1801
We proposed a sub-intrapersonal space method for face recognition.
Songcan Chen, Jun Liu and Zhihua Zhou
Pattern Recognition 37, no. 7 (2004): 1553-1555
Some useful sources on this topic:
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