Attention-Oriented Two-Stream Convolutional Networks for the Identification of Face Spoofing

Face recognition has been extensively researched and has seen tremendous success in a variety of applications over the past few decades because the human face retains the most information for identifying individuals. Modern face recognition systems are still vulnerable to face spoofing attacks, such as the face video replay attack. Despite the fact that numerous efficient antispoofing techniques have been put forth, we discover that illuminations impair the effectiveness of many of the current techniques. It encourages us to create illumination-invariant anti-spoofing techniques. In this work, we propose a two stream convolutional neural network (TSCNN) that operates on two complementary spaces: multi-scale retinex (MSR) space (illumination invariant space) and RGB space (original imaging space). In particular, MSR is invariant to illumination but contains less detailed facial information than RGB space, which contains detailed facial textures but is sensitive to illumination. Furthermore, the high-frequency information that is discriminative for face spoofing detection can be efficiently captured by MSR images. To learn the discriminative features for anti-spoofing, the TSCNN is fed images from two spaces. We suggest an attention-based fusion technique that can successfully capture the complementarity of two features in order to smoothly fuse the features from two sources (RGB and MSR). We test the suggested framework on a number of databases, including OULU, CASIA-FASD, and REPLAY-ATTACK, and we obtain very competitive results. We perform cross-database experiments to further confirm the suggested strategies' capacity for generalization, and the outcomes demonstrate the high efficacy of our approach.