Abstract

Biometric systems have witnessed a large scale deployment in a wide range of security applications. Among the available biometric modalities, iris recognition is one of the most promising and widely adopted modalities. But this iris recognition system remains a challenge due to different presentation attacks that fail to assure the reliability when adopting these systems in real-life. This paper, presented a deep analysis of presentation attacks on iris recognition systems especially focusing on the photo print attacks. Also novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines. The input iris image is divided in to periocular and iris region then convert in to different scales or resolutions then the features are computed from the multiscale converted image then the Support vector machine classifier gives the right result according to the input image. The performance of the proposed PAD scheme is well suitable for real time scenario. Extensive experiments are carried out on four different publicly available iris artefact databases that have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well-established stateof-the-art schemes. Finally find the person who are in the database and also can find the specific person.