Wavelet Maxima and Moment Invariants Based Iris Feature Extraction

Abstract

Iris recognition is one of the most reliable personal identification methods and is becoming the most promising technique for high security. In this paper, we propose an efficient method for personal iris identification by investigating iris textures that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform (wavelet maxima components), and moment invariants. The proposed scheme is invariant to translation, rotation, and scale changes. Experimental results have shown that the proposed system could be used for personal identification in an efficient and effective manner.Index Terms-multiscale edge detection, iris feature extraction, wavelet maxima, moment invariants.

Iris verification system based on curvelet transform

“…Another research choose to encode iris on base of statistical features like Nabti et al [14] who calculated the 7 invariant moments to generate an iris features vector. Also, in [9], the authors proposed to extract the some statistical features of the iris circles in order to encode an iris.…”