Ear Recognition for Automated Human Identification

Abstract

This paper investigates a new approach for the automated human identification using ear imaging. We present a completely automated approach for the robust. Segmentation of curved region of interest using morphological operator sand Fourier descriptors. We also investigate new feature extraction approach for ear identification using localized orientation information and also examine local gray- level phase information using complex Gabor filters. Our investigation develops a computationally attractive an defective alternative to characterize the automatically. Segmented ear images using a pair of log Gabor filters. The experimental results achieve average rank-one recognition accuracy of 96.27% and 95.93%, respectively, on the publicly available database of 125 and 221 subjects. Our experimental results from the authentication experiments and false positive identification verses false negative identification also suggest the superiority of the proposed approach over the other popular feature extraction approach considered in this work.