Ear recognition based on kernel principal component analysis and support vector machine

Some key issues in ear recognition were investigated. Two ear extraction and normalization methods, the mark line (long axis of the outer ear contour) based method and the mark points (the start and end points of the outer ear contour) based method, were proposed for recognizing ear images in the USTB ear database. Based on the analysis of the recent advances in ear recognition methods, the kernel principal component analysis (KPCA) was applied for ear feature extraction, and the support vector machine (SVM) model was applied for ear recognition. The ear recognition rate on USTB ear database with pose variation and lighting variation was 98.7%. The experimental result indicates the effectiveness of this method and proves the feasibility of ear recognition to be used in the field of personal authentication.