Facial skin blood perfusion change based liveness detection using video images

Abstract

Facial recognition has been employed as a user-friendly person authentication method, and facial spoofing attack has become a common problem. Although there are two kinds of facial spoofing attacks, 2D spoofing and 3D spoofing, almost studies evaluate the performance only for 2D spoofing. Temporal change of face color will be a possible characteristic to detect liveness against to 3D spoofing attack because there is a relationship between the skin blood perfusion change and the temporal color change in facial video images. This paper proposes two features, R-G correlation feature and interarea correlation feature, to detect liveness using video images. Also, liveness detection method using support vector machine is demonstrated. The performance was evaluated by accuracy (ACC) for classifying liveness face and three types of spoofing face—2D printed image, 2D monitor image, and 3D doll. The ACC was 99.2% at the lighting condition of room light, 99.5% at sunlight illuminating the face, and 98.6% at sunlight illuminating the back of the head.