Rapid and brief communication: Two-dimensional discriminant transform for face recognition

Authors:
Jian Yang;David Zhang;Xu Yong;Jing-yu Yang
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong and Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China
Venue:
Pattern Recognition
Year:
2005

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Abstract

This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition.