Effective classification image space which can solve small sample size problem

Authors:
Yu-jie Zheng;Jing-yu Yang;Jian Yang;Xiao-jun Wu
Affiliations:
Nanjing University of Science and Technology, China;Nanjing University of Science and Technology, China;Hong Kong Polytechnic University, Kowloon, Hong Kong;Jiangsu University of Science and Technology, Zhenjiang 212003, P.R.China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

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DLDA/QR: a robust direct LDA algorithm for face recognition and its theoretical foundation

PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining

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Abstract

Linear Discriminant Analysis (LDA) is one of the most popular methods in feature extraction and dimension reduction. However, in many real applications, particularly in image recognition applications such as face recognition, conventional LDA algorithm will often encounter small sample size problem. In this paper, an effective classification image space is defined and optimal features are extracted from this space. With the proposed method, an effective classification image space of each original image is first obtained. Then, optimal features are extracted from this space. The small sample size problem is solved effectively with the proposed method. Experimental results on XM2VTS face database demonstrate the effectiveness of the proposed method.