Robust contact lens detection using local phase quantization and binary gabor pattern

Abstract

Due to its resistance to circumvention, iris has been used as a prime biometric trait in border crossings and identity related civil projects. However, sensor level spoofing attacks such as the use of printed iris, plastic eyeballs and contact lens pose a challenge by helping intruders to sidestep security in iris based biometric systems. Attacks through contact lenses are most challenging to detect as they obfuscate the iris partially and part of original iris remains visible through them. In this paper, we present a contact lens dataset containing 12823 images acquired from 50 subjects. Each subject has images pertaining to no lens, soft lens and cosmetic lens class. Verification results with three different techniques on three datasets suggest an average degradation of 3.10% in EER when subject is wearing soft lens and 17.34% when subject is wearing cosmetic lens. Further we propose a cosmetic lens detection approach based on Local Phase Quantization(LPQ) and Binary Gabor Pattern(BGP). Experiments conducted on publicly available IIITD Vista, IIITD Cogent, ND 2010 and self-collected dataset indicate that our method outperforms previous lens detection techniques in terms of Correct Classification Rate and false Acceptance Rate. The results suggest that a comprehensive texture descriptor having blur tolerance of LPQ and robustness of BGP is suitable for cosmetic lens detection.