Face anti-spoofing using Haralick features

Face spoofing can be performed in a variety of ways such as replay attack, print attack, and mask attack to deceive an automated recognition algorithm. To mitigate the effect of spoofing attempts, face anti-spoofing approaches aim to distinguish between genuine samples and spoofed samples. The focus of this paper is to detect spoofing attempts via Haralick texture features. The proposed algorithm extracts block-wise Haralick texture features from redundant discrete wavelet transformed frames obtained from a video. Dimensionality of the feature vector is reduced using principal component analysis and two class classification is performed using support vector machine. Results on the 3DMAD database show that the proposed algorithm achieves state-of-the-art results for both frame-based and video-based approaches, including 100% accuracy on video-based spoofing detection. Further, the results are reported on existing benchmark databases on which the proposed feature extraction framework archives state-of-the-art performance.