CNN-LPQ: convolutional neural network combined to local phase quantization based approach for face anti-spoofing

In this paper, we propose a novel approach for face spoofing detection using a combination of color texture descriptors with a new convolutional neural network (CNN) architecture. The proposed approach is based on a new convolutional neural network architecture composed of two CNN parallel branches. The first branch is fed with complementary shallow local phase quantization (LPQ) invariant descriptors that result from joint color texture information from the hue, saturation, and value (HSV) color space to accurately capture the reflection properties of the face. Combining the HSV color space with LPQ is known to significantly improve performance. The second branch of the CNN takes an RGB image directly as input, effectively separating chromatic (color-related) information from achromatic (brightness-related) information in order to extract crucial facial color features. Each branch of the CNN produces a vector of deep features that are extracted. To effectively concatenate the deep features from the two output branches, we employ an attention mechanism based combination method. This method captures the complementarity of the two branches, improving the accuracy and robustness of the model. The combined feature vectors form an input vector for the next Dense layer, where the model can distinguish between live and spoofed faces. Our method detects 2D facial spoofing attacks involving printed photos and replayed videos. We showcase the effectiveness and superior performance of our approach through a series of experiments conducted on both the CASIA-FASD and Replay-Attack datasets. Our results are promising and surpassing those of other state-of-the-art methods on both used datasets in terms of 9 performance metrics.