Abstract
A method combining eigenface with different wavelet subbands for face recognition is proposed. Each training image is decomposed into multi-subbands for extracting their eigenvector sets and projection vectors. In the recognition process, the inner product distance between the projection vectors of the test image and that of the training image are calculated. The training image, corresponding to the maximum distance under the given threshold condition, is considered as the final result. The experimental results on the ORL and YALE face database show that, compared with the eigenface method directly on the image domain or on a single wavelet subband, the recognition accuracy using the proposed method is improved by 5% without influencing the recognition speed.
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References
M. Turk and A. Pentland,Journal of Cognitive Neuroscience,3 (1991), 71.
P.C. Yuela, D.Q. Dai, and G.C. Feng,IEEE Southwest Symposium on Image analysis and Interpretation 1998, 223.
G.C. Feng, P.C. Yuen, and D.Q. Dai,J. Electron. Imaging,9 (2001), 226.
C. Garcia, G. Zikos, and G. Tziritas,Image Vision Computing,18 (2000), 289.
Bai-Ling Zhang, Haihong Zhang, and Shuzhi Sam Ge,IEEE Trans. on Neural Network,15 (2004), 196.
I. Daubechies,IEEE Trans. Inf. Theory,36 (1990), 961.
C. Nasta, b. Moghaddam, and A. Pentland,Comput. Vis. Image Underst,65 (1997), 179.
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Supported by Shanghai Science and Technology Development Foundation (04D02-1)