Design And Implementation Of Face Authenticity Identification Algorithm Based On Attention Mechanis

Face analysis,as a highly anticipated research direction in the field of Computer Vision,has various derivative technologies,among which face detection and recognition are the branches of technology explored by many researchers.The development of these technologies has brought convenience to the public’s lives,and people are no longer rely on traditional payment methods,but are more inclined to use facial payment.Subsequently,there are security issues with face recognition systems,as unprotected systems are highly vulnerable to attacks by criminals.The technology of face antispoofing was born from this.It’s an intermediate technology between face detection and face identity recognition,helping face recognition systems cope with different types of representation attacks.This has important application value in access control systems,intelligent payments,and criminal investigation scenarios.Taking this as the research background,this article conducts the following research:(1)Based on the research on the development of face detection technology in recent years,combined with the advantages of attention mechanism,a context-aware face detection model Face-SSD is designed to address the issues of redundant and high computational complexity in face detection models;(2)Design a feature fusion module on the basis of Face-SSD to optimize the model,ensuring that the model complexity is controlled within the ideal range while improving the model detection accuracy;(3)With the idea of transfer learning,the classification structure of Vision Transformer model is improved and discriminant marks are embedded,and FAS-Vi T model is proposed,which is suitable for the task of face anti-spoofing;(4)Based on the FAS-Vi T model,a face anti-spoofing model based on dual channel feature fusion is designed to address the lighting factors that directly affect the face antispoofing task in different datasets.The model combines the texture information of RGB images and the face image information with illumination consistency processed by image enhancement algorithms to obtain richer identification clues.For the model proposed in this article,experiments were designed to verify its performance,and the state-of-the-art methods were selected for comparison.The experimental results proved the effectiveness and generalization of applying different types of attention to face detection and face anti-spoofing tasks in this article.