Masquerade Attack Analysis for Secured Face Biometric System
Shweta Policepatil1, Sanjeeva Kumar M. Hatture2
1Shweta Policepatil*, Pursuing, Master Degree, Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot, Visvesvaraya Technological University, Belagavi ( Karnataka), India.
2Sanjeeva Kumar M. Hatture, Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot, Visvesvaraya Technological University, Belagavi ( Karnataka), India.
Manuscript received on July 20, 2021. | Revised Manuscript received on July 29, 2021. | Manuscript published on July 30, 2021. | PP: 225-232 | Volume-10 Issue-2, July 2021. | Retrieval Number: 100.1/ijrte.B63090710221| DOI: 10.35940/ijrte.B6309.0710221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Biometrics systems are mostly used to establish an automated way for validating or recognising a living or non-living person’s identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today’s world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.
Keywords: Face Recognition, Pattern Recognition, Feature Extraction, Anti-Spoofing, Masquerade Attack.