Weighted SOM-Face: selecting local features for recognition from individual face image

Authors:
Xiaoyang Tan;Jun Liu;Songcan Chen;Fuyan Zhang
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

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Abstract

In human face recognition, different facial regions have different degrees of importance, and exploiting such information would hopefully improve the accuracy of the recognition system. A novel method is therefore proposed in this paper to automatically select the facial regions that are important for recognition. Unlike most of previous attempts, the selection is based on the facial appearance of individual subjects, rather than the appearance of all subjects. Hence the recognition process is class-specific. Experiments on the FERET face database show that the proposed methods can automatically and correctly identify those supposed important local features for recognition and thus are much beneficial to improve the recognition accuracy of the recognition system even under the condition of only one single training sample per person.