Ear Recognition using Dual Tree Complex Wavelet Transform

Abstract

Since last 10 years, various methods have been used for ear recognition. This paper describes the automatic localization of an ear and it’s segmentation from the side poses of face images. In this paper, authors have proposed a novel approach of feature extraction of iris image using 2D Dual Tree Complex Wavelet Transform (2D-DT-CWT) which provides six sub-bands in 06 different orientations, as against three orientations in DWT. DT-CWT being complex, exhibits the property of shift invariance. Ear feature vectors are obtained by computing mean, standard deviation, energy and entropy of these six sub-bands of DT-CWT and three sub-bands of DWT. Canberra distance and Euclidian distance are used for matching. This method is implemented and tested on two image databases, UND database of 219 subjects from the University of Notre Dame and own database created at MCTE, of 40 subjects which is also used for online ear testing of system for access control at MCTE. False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) and Receiver’s Operating Curve (ROC) are compiled at various thresholds. The accuracy of recognition is achieved above 97 %.