Face Anti-spoofing Using Deep Dual Network

Abstract

Face recognition has been adopted widely for real-world applications because of its convenience and contactless nature. On the other hand, forged faces for spoofing attacks can be fabricated easily using a variety of materials, such as pictures, high-resolution videos, and printed masks, etc., which pose a great threat to face-based recognition systems. Therefore, face antispoofing has become an essential technique to achieve high-level security. Although many studies have explored effective features to discriminate live faces from fake ones, even with deep neural networks, they still struggle to grasp meaningful differences from a single image because of the sophisticated spoofing attacks with various media. This paper proposes a novel method for face anti-spoofing based on stereo facial images. Because the three-dimensional structure of a live face clearly yields a structural difference in the image pair taken by a stereo camera, whereas significant differences do not occur in fake faces of two-dimensional planes, this paper proposes to learn the differences of left-right image pairs in the latent space of a deep neural network. One important advantage of the proposed method is that the structural difference is encoded implicitly in a nonlinear manner through the deep architecture without explicitly computing the disparity. The experimental results on a constructed dataset revealed the proposed method to be effective for diverse spoofing attacks.