LAUSR

Biometric-based verification system has emerged as a powerful authentication tool. Despite its advantages over traditional systems, it is prone to several attacks. These attacks may creep through the biometric system and may prove fatal if it is not robust enough. One such attack, known as replay attack, relates to replaying of illegally intercepted data has been least explored with respect to biometrics. The paper proposes a non-deterministic approach to iris recognition and attempts to show its utility in allaying replay attack over iris recognition system. The system determines robust iris regions for each eye using LBP-based feature extraction and involves the use of randomly selected subsets of these regions for authentication. These data, even if intercepted, are useless as the non-deterministic nature of technique will require a differently ordered subset of regions for each authentication. The performance of this system and its effectiveness in allaying replay attack has been shown experimentally. The results have been compared with existing state-of-art techniques with respect to iris recognition and replay attack. The impact of hill climbing attack on the proposed approach has also been discussed as it has been proved, by various researchers, to be critical to the performance of a biometric system.