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Face Anti-Spoofing Using Texture-Based Techniques and Filtering Methods

Hasan, Md. Rezwan, Hasan Mahmud, S. M., Li, Xiang Yu (2019) Face Anti-Spoofing Using Texture-Based Techniques and Filtering Methods. Journal of Physics: Conference Series, 1229 . Article Number 012044. ISSN 1742-6588. E-ISSN 1742-6596. (doi:10.1088/1742-6596/1229/1/012044) (KAR id:87135)

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Official URL
https://doi.org/10.1088/1742-6596/1229/1/012044

Abstract

User authentication for an accurate biometric system is the demand of the hour in today's world. When somebody attempts to take on the appearance of another person by introducing a phoney face or video before the face detection camera and gets illegitimate access, a face presentation attack usually happens. To effectively protect the privacy of a person, it is very critical to build a face authentication and anti-spoofing system. This paper introduces a novel and appealing face spoof detection technique, which is primarily based on the study of contrast and dynamic texture features of both seized and spoofed photos. Valid identification of photo spoofing is anticipated here. A modified version of the DoG filtering method, and local binary pattern variance (LBPV) based technique, which is invariant to rotation, are designated to be used in this paper. Support vector machine (SVM) is used when feature vectors are extracted for further analysis. The publicly available NUAA photo-imposter database is adapted to test the system, which includes facial images with different illumination and area. The accuracy of the method can be assessed using the false acceptance rate (FAR) and false rejection rate (FRR). The results express that our method performs better on key indices compared to other state-of-the-art techniques following the provided evaluation protocols tested on a similar dataset.

Item Type: Article
DOI/Identification number: 10.1088/1742-6596/1229/1/012044
Uncontrolled keywords: Face Anti-Spoofing, Spoof Detection, Face Liveness Detection, Biometric Security
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1653 Human face recognition
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.B56 Biometric identification
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Md Hasan
Date Deposited: 15 Mar 2021 16:14 UTC
Last Modified: 16 Mar 2021 10:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/87135 (The current URI for this page, for reference purposes)
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