Face recognition based on discriminant fractional Fourier feature extraction

Authors:
Xiao-Yuan Jing;Hau-San Wong;David Zhang
Affiliations:
Shenzhen Graduate School of Harbin Institute of Technology, Xili, Shenzhen, Guangdong, 518055, China;Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

Developed from the conventional Fourier transform, the fractional Fourier transform is a powerful signal analysis and processing technique. In this paper, we apply it to the field of face recognition. By combining it with the discrimination analysis technique, we propose a new face recognition approach. First, we use a two-dimensional separability judgment to select appropriate value of angle parameter for discrete fractional Fourier transform. Second, we present a reformative Fisherface method to extract discriminative features from the preprocessed images and perform the classification using the nearest neighbor classifier. Experimental results on two public face databases indicate that our approach outperforms four representative discrimination methods. It obtains better and robust classification effects.