In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient liveness detection algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible light camera, the proposed algorithm takes two facial photos, one taken with a flash, the other without a flash. The proposed @math descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with @math descriptor outperforms other flash-based liveness detection algorithms on both an in-house database and on publicly available NUAA and Replay-Attack databases. Moreover, the proposed algorithm achieves comparable accuracy to that of an end-to-end, deep neural network classifier, while being approximately ten-times faster execution speed.
Recent studies point out that spoofing attacks using facial masks still are a severe problem for current biometric face recognition (FR) systems. As such systems are becoming more frequently used, for example, for automated border crossing or access control to critical infrastructure, advanced anti-spoofing techniques are necessary to counter these attacks. This work presents a novel, cross-modal approach that enhances existing solutions for face verification and uses multispectral short wave infrared (SWIR) imaging to ensure the authenticity of a face even in the presence of partial disguises and masks. It is evaluated on a dataset containing 137 subjects and a variety of spoofing attacks. Using a commercial FR system, it successfully rejects all attempts to counterfeit a foreign face with a false acceptance rate FAR cf = 0 and most attempts to disguise the own identity with FAR dg = 1 at a false rejection rate of FRR < 5 using SWIR images for verification. With the advanced 3D reconstruction and printing technologies, creating a super-real 3D facial mask becomes feasible at an affordable cost. This brings a new challenge to face presentation attack detection (PAD) against 3D facial mask attack. As such, there is an urgent need to solve this problem as many face recognition systems have been deployed in real-world applications. Since this is a relatively new research problem, few studies has been conducted and reported. In order to attract more attentions on 3D mask face PAD, this book chapter summarizes the progress in the past few years, as well as publicly available datasets. Finally, some open problems in 3D mask attack are discussed. 3D mask spoofing attack has been one of the main challenges in face recognition. Among existing methods, texture-based approaches show powerful abilities and achieve encouraging results on 3D mask face anti-spoofing. However, these approaches may not be robust enough in application scenarios and could fail to detect imposters with hyper-real masks. In this paper, we propose a novel approach to 3D mask face anti-spoofing from a new perspective, by analysing heartbeat signal through remote Photoplethysmography (rPPG). We develop a novel local rPPG correlation model to extract discriminative local heartbeat signal patterns so that an imposter can better be detected regardless of the material and quality of the mask. To further exploit the characteristic of rPPG distribution on real faces, we learn a confidence map through heartbeat signal strength to weight local rPPG correlation pattern for classification. Experiments on both public and self-collected datasets validate that the proposed method achieves promising results under intra and cross dataset scenario.