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 phony 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.
Highlights
The performance of face detection and recognition systems have improved drastically in the last few years
To test the effectiveness of our proposed approach, we evaluated two different types of spoof detection methods: LTP (Local Ternary Pattern) features, and modified Difference of Gaussian (DoG)-local binary pattern variance (LBPV) features
The rate of face liveness detection on test sets in the dataset is displayed in Table 1. in terms of Half Total Error Rate (HTER), Accuracy, and Area Under the Curve (AUC) along with False Rejection Rate (FRR) and False Acceptance Rate (FAR) at fixed threshold value of ( = 5) for the LTP method
Summary
The performance of face detection and recognition systems have improved drastically in the last few years. This innovation is currently considered as a developed system and is used in numerous real-world applications from banking security to smart house systems and device authentication. Many face liveness detection techniques have been proposed to restrain the face recognition systems against this kind of occurrences. These techniques have shown good performances on the existing face presentation attack databases. Their performances deteriorate radically under real-world variations (e.g., illumination and camera device variations)
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