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.
Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers a diverse range of potential attack variations. Specifically, the database contains 50 genuine subjects, and fake faces are made from the high quality records of the genuine faces. Three imaging qualities are considered, namely the low quality, normal quality and high quality. Three fake face attacks are implemented, which include warped photo attack, cut photo attack and video attack. Therefore each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects. A baseline algorithm is also given for comparison, which explores the high frequency information in the facial region to determine the liveness. We hope such a database can serve as an evaluation platform for future researches in the literature. 3D Mask face spoofing attack becomes new challenge and attracts more research interests in recent years. However, due to the deficiency number and limited variations of database, there are few methods be proposed to aim on it. Meanwhile, most of existing databases only concentrate on the anti-spoofing of different kinds of attacks and ignore the environmental changes in real world applications. In this paper, we build a new 3D mask anti-spoofing database with more variations to simulate the real world scenario. The proposed database contains 12 masks from two companies with different appearance quality. 7 Cameras from the stationary and mobile devices and 6 lighting settings that cover typical illumination conditions are also included. Therefore, each subject contains 42 (7 cameras * 6 lightings) genuine and 42 mask sequences and the total size is 1008 videos. Through the benchmark experiments, directions of the future study are pointed out. We plan to release the database as an platform to evaluate methods under different variations. Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15 Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types. The problem of detecting face spoofing attacks (presentation attacks) has recently gained a well-deserved popularity. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. In this paper, we inspect the spoofing potential of subject-specific 3D facial masks for 2D face recognition. Additionally, we analyze Local Binary Patterns based coun-termeasures using both color and depth data, obtained by Kinect. For this purpose, we introduce the 3D Mask Attack Database (3DMAD), the first publicly available 3D spoofing database, recorded with a low-cost depth camera. Extensive experiments on 3DMAD show that easily attainable facial masks can pose a serious threat to 2D face recognition systems and LBP is a powerful weapon to eliminate it. Spoofing with photograph or video is one of the most common manner to circumvent a face recognition system. In this paper, we present a real-time and non-intrusive method to address this based on individual images from a generic webcamera. The task is formulated as a binary classification problem, in which, however, the distribution of positive and negative are largely overlapping in the input space, and a suitable representation space is hence of importance. Using the Lambertian model, we propose two strategies to extract the essential information about different surface properties of a live human face or a photograph, in terms of latent samples. Based on these, we develop two new extensions to the sparse logistic regression model which allow quick and accurate spoof detection. Primary experiments on a large photo imposter database show that the proposed method gives preferable detection performance compared to others. A common technique to by-pass 2-D face recognition systems is to use photographs of spoofed identities. Unfortunately, research in counter-measures to this type of attack have not kept-up - even if such threats have been known for nearly a decade, there seems to exist no consensus on best practices, techniques or protocols for developing and testing spoofing-detectors for face recognition. We attribute the reason for this delay, partly, to the unavailability of public databases and protocols to study solutions and compare results. To this purpose we introduce the publicly available PRINT-ATTACK database and exemplify how to use its companion protocol with a motion-based algorithm that detects correlations between the person's head movements and the scene context. The results are to be used as basis for comparison to other counter-measure techniques. The PRINT-ATTACK database contains 200 videos of real-accesses and 200 videos of spoof attempts using printed photographs of 50 different identities.