Improved method of ear recognition based on tensor PCA
Haijun Zhang · Computer Engineering and Applications Journal · 2011
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 ofLL 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.