Multifeature Fusion Detection Method for Fake Face Attack in Identity Authentication

With the rise in biometric-based identity authentication, facial recognition software has already stimulated interesting research. However, facial recognition has also been subjected to criticism due to security concerns. The main attack methods include photo, video, and three-dimensional model attacks. In this paper, we propose a multifeature fusion scheme that combines dynamic and static joint analysis to detect fake face attacks. Since the texture differences between the real and the fake faces can be easily detected, LBP (local binary patter) texture operators and optical flow algorithms are often merged. Basic LBP methods are also modified by considering the nearest neighbour binary computing method instead of the fixed centre pixel method; the traditional optical flow algorithm is also modified by applying the multifusion feature superposition method, which reduces the noise of the image. In the pyramid model, image processing is performed in each layer by using block calculations that form multiple block images. The features of the image are obtained via two fused algorithms (MOLF), which are then trained and tested separately by an SVM classifier. Experimental results show that this method can improve detection accuracy while also reducing computational complexity. In this paper, we use the CASIA, PRINT-ATTACK, and REPLAY-ATTACK database to compare the various LBP algorithms that incorporate optical flow and fusion algorithms.