Face recognition based on multi-scale singular value features

Authors:
Ran Jin;Zhuojun Dong
Affiliations:
Department of computer science and information technology, Zhejiang Wanli University, Ningbo, Zhejiang, China;Department of computer science and information technology, Zhejiang Wanli University, Ningbo, Zhejiang, China
Venue:
WSEAS Transactions on Computers
Year:
2009

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Abstract

Singular value vecto r of an image is a valid feature for identification. But the recognition rate is low when only one scale singular value vector is used for face recognition. An algorithm was developed to improve the recognition rate. Many subimages are obtained when the face image is divided in different scales, with all singular values of each subimage organized and used as an eigenvector of the face image. Faces are then verified by linear discriminant analysis (LDA) under these multiscale singular value vectors. These multiscale singular value vectors include all features of an image from local to the whole, so more discriminant information for pattern recognition is obtained. Experiments were made with ORL human face image databases. The experimental results show that the method is obviously superior to the corresponding algorithms with a recognition rate of 97.38%.