Face Recognition based on Two-Dimensional PCA on Wavelet Subband
نویسندگان
چکیده
In this paper a new face recognition technique based on Two-Dimensional Principal Component Analysis (2DPCA) on Wavelet Subband is proposed. We extract image features of facial images from various wavelet transforms (Haar, Daubechies, Coiflet, Symlet, Biothogonal and Reverse Biorthogonal) by decomposing face image in subbands 1 to 8. These features are analyzed by 2DPCA and Euclidean distance measure. A series of experiments based on ORL database were then performed to evaluate the performance. The results show that for the entire wavelets, subband 3 give the best accuracy and is computationally most efficient. 7th order Symlet is found to be the best among all.
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