Research on Face Anti-Spoofing Algorithm Based on Image Fusion

Along with the rapid development of biometric authentication technology, face recognition has been commercially used in many industries in recent years. However, it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs, videos or forged 3D masks. In order to solve this problem, this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images. The normal temperature distribution of the human face is stable and characteristic, and the important physiological information of the human body can be observed by the infrared thermal images. Therefore, based on the thermal infrared image, the pixel value of the pulse sensitive area of the human face is collected, and the human heart rate signal is detected to distinguish between real faces and spoofing faces. In order to better obtain the texture features of the face, an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed. Firstly, DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands. Then, the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high- and low-frequency subbands. Finally, the DTCWT inverse transform is used to obtain the fused image containing the facial texture features. Face recognition is carried out on the fused image to realize identity authentication. Experimental results show that this algorithm can effectively resist attacks from photos, videos or masks. Compared with the use of visible light images alone for face recognition, this algorithm has higher recognition accuracy and better robustness.