An Improved Iris Recognition System Using Feature Extraction Based on Wavelet Maxima Moment Invariants

Abstract

Abstract. Human recognition technology based on biometrics has received increasing attention over the past decade. Iris recognition is considered to be the most reliable biometric authentication system and is becoming the most promising technique for high security. In this paper, we propose a multiscale approach for iris localization by using wavelet modulus maxima for edge detection, a fast and a compact method for iris feature extraction based on wavelet maxima components and moment invariants. The features are represented as feature vector, thus allowing us to also propose a fast matching scheme based on exclusive OR operation. Experimental results have shown that the performance of the proposed method is very encouraging and comparable to the well known methods used for iris texture analysis.

Improved Iris Recognition in 2D Eigen Space

“…Paper [12] proposes an algorithm to localize the iris more quickly by improved Hough transform. In [13] Wavelet maxima moment in variant is used for iris feature extraction. A shape guided approach using game theory has been used for iris detection in [14].…”

Retinal vessel tree as biometric pattern

“…Many different human biometrics have been used to build a valid template for verification and identification tasks. Among the most common biometrics, we can find the fingerprint Bolle et al (2002); Maio & Maltoni (1997); Seung-Hyun et al (1995); Venkataramani & Kumar (2003), iris Chou et al (2006); He et al (2008); Kim, Cho, Choi & Marks (2004); Ma et al (2002); Nabti & Bouridane (2007) or face Kim, Kim, Bang & Lee (2004); Kisku et al (2008); Mian et al (2008); Moghaddam & Pentland (1997); Yang et al (2000) or hand geometry Jain et al (1999); Sidlauskas (1988); systems Lab (n.d.); Zunkel (1999). However, there exist other emerging biometrics where we can find retina biometrics.…”