Iris Anti-Spoofing Using Image Quality Measures

Spoof detection is a critical issue for the recognition of iris because it reduces the risk of forging iris recognition systems. The most relevant iris spoofing attacks reported in previous studies follows one of the three trends: photo attacks, contact-lens attacks or artificial-eye attacks. Spoofing attacks have prompted the biometric research community to learn more about the threat posed by these kinds of attacks on iris, fingerprint and face biometric systems. In this thesis, various Image Quality Assessment techniques to detect fake and real iris images presented to biometric systems were used. In this context, full reference image quality assessment measures such as Error Sensitivity Measures, Structural Similarity Measures and Information Theoretic Measures are implemented to distinguish fake and real iris images. Full-reference Image Quality Measures are also concatenated using feature-level fusion strategy. We propose to fuse twenty one full-reference image quality measures for iris anti-spoofing against print-attacks, contact-lens attacks and artificial-eye attacks. In order to evaluate the performance of the proposed iris anti-spoofing method using feature-level fusion of Image Quality Assessment techniques, two publicly available databases, namely CASIA and IIITD, were used. A comparative analysis of the performance of these Image Quality Assessment metrics is performed towards the completion of the thesis on various iris spoofing datasets of the aforementioned iris spoofing databases.