Title: Object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier

Authors: K. Mohan; P. Chandrasekhar; K.V. Ramanaiah

Addresses: Vel Tech Dr.RR & Dr.SR University, Avadi, Chennai, India ' Department of Electrical Engineering, Vel Tech Dr.RR & Dr.SR University, Avadi, Chennai, India ' Department of E.C.E., Vogivemana University, A.P, India

Abstract: The object face liveness detection for genuine face recognition and user authentication is a difficult task and day to day it becoming an interesting tricky in real time vision and security applications. Since many decades, various authors have proposed new technique and methods and developed but still the system has to improve to recognise the genuine object faces from spoofing objects with increasing in accuracy. However, in account of existing methods were fails in finding of genuine objects from sample objects and individual differences between them. The ordinary classifier cannot simplifies well to various kind of objects in different directions especially in case of blur images. In order to overcome his problem, we proposed an object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier, allows to select specific area from whole object, extract features from specific area of object leads reduction in processing time and complexity in feature extraction. Later the system recognise respective faces, finally it checks for live objects with the help of fuzzy logic-based SVM classifier. With these proposed system, makes it practical to train well performed individual object to its certain face with liveness detection and achieved improvement in performance and accuracy.

Keywords: object-specific face; genuine object; spoofing objects; liveness detection; authentication; anti-spoofing; feature extractors; region of interest; HOG-LPQ descriptors; Fuz-SVM classifier.

DOI: 10.1504/IJCAET.2020.106213

International Journal of Computer Aided Engineering and Technology, 2020 Vol.12 No.3, pp.287 - 300

Received: 18 Sep 2017
Accepted: 30 Oct 2017
Published online: 02 Apr 2020 *

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