Video Presentation Attack Detection in Visible Spectrum Iris Recognition Using Magnified Phase Information

• Published in: IEEE transactions on information forensics and security Vol. 10; no. 10; pp. 2048 - 2056
• Main Authors: Raja, K. B., Raghavendra, R., Busch, C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; Biometric recognition systems; Biometrics; Classification; Computer information security; Construction; Iris recognition; Modules; Presentation attack detection; Protocols; Replay attack; Robustness; Smart phones; Spoof; Testing; Visible Iris; Visible spectrum; spoof; visible iris; biometrics; Replay attack; iris recognition; presentation attack detection
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2015.2440188

The gaining popularity of the visible spectrum iris recognition has sparked the interest in adopting it for various access control applications. Along with the popularity of visible spectrum iris recognition comes the threat of identity spoofing, presentation, or direct attack. This paper presents a novel scheme for detecting video presentation attacks in visible spectrum iris recognition system by magnifying the phase information in the eye region of the subject. The proposed scheme employs modified Eulerian video magnification (EVM) to enhance the subtle phase information in eye region and novel decision module to classify it as artefact(spoof attack) or normal presentation. The proposed decision module is based on estimating the change of phase information obtained from EVM, specially tailored to detect presentation attacks on video-based iris recognition systems in visible spectrum. The proposed scheme is extensively evaluated on the newly constructed database consisting of 62 unique iris video acquired using two smartphones-iPhone 5S and Nokia Lumia 1020. We also construct the artefact database with 62 iris acquired by replaying normal presentation iris video on iPad with retina display. Extensive evaluation of proposed presentation attack detection (PAD) scheme on the newly constructed database has shown an outstanding performance of average classification error rate = 0% supporting the robustness of the proposed PAD scheme.