Non-Decimated Wavelet Based Multi-Band Ear Recognition Using Principal Component Analysis

Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a 2D Wavelet based Multi-Band (2D-WMBPCA) recognition method, inspired by PCA techniques for multispectral and hyperspectral images. The proposed 2D-WMBPCA method performs non-decimated wavelet transform on the input image, dividing it into its subbands. Each resulting subband is then divided number of frames coefficient’s values. multi frame generation boundaries are calculated using either equal size or greedy hill climbing techniques. Conventional each subband’s frames, yielding eigenvectors, which used matching. intersection energy eigenvectors total features shows bands yield highest matching performance. Experimental results images two benchmark datasets, called IITD II USTB I, demonstrated that technique significantly outperforms Single Image up 56.79% eigenfaces 20.37% with respect accuracy. Furthermore, achieves very competitive those learning at fraction their computational time without needing be trained.