关键词: 张量主成分分析, 小波变换, 人耳识别, 支持向量机

Abstract: Tensor Principal Component Analysis（TPCA） is a new Principal Component Analysis（PCA） method，which can solve the problem when image dimension is reduced by conventional Principal Component Analysis.Wavelet transform has good time-frequency analysis features and plays a dimension reduction role.According to the two advantages of the above algorithms，a new ear recognition algorithm based on Tensor Principal Component Analysis is proposed.Wavelet transform is firstly used and obtains four sub-band images.Tensor Principal Component Analysis is used to extract the feature of “LL” low frequency sub-band images.Support Vector Machine（SVM） method is used to identify.Experimental results show that the method compared with conventional Principal Component Analysis improves the recognition rate and shortens the identification time.On the USTB ear database test，the recognition rate of the proposed algorithm is 6% higher than that of the conventional Principal Component Analysis（PCA） algorithm，and the recognition time of the proposed algorithm is 35.23% of the conventional Principal Component Analysis（PCA） algorithm.

Key words: Tensor Principal Component Analysis（TPCA）, wavelet transform, ear recognition, Support Vector Machine（SVM）