Face Recognition Method in Complex Environment Based on Infrared Visible Fusion

With the development of deep learning methods, the accuracy and speed of face recognition based on visible light in ideal environments have reached an excellent level. However, in complex environments such as low light, the lack of a light source keeps visible images from reflecting face details, resulting in reduced or even invalid face recognition. Aiming at the problems in this issue, a face recognition method in complex environments based on infrared-visible light fusion is proposed. Firstly, an infrared and visible fusion recognition network combining CNN and Transformer is introduced, specifically designed for low illumination environments. This network combines CNN and visual Transformer in parallel to form a single-mode feature fusion module, which is utilized to effectively utilize local details and global context information from the source image. Additionally, a multimodal feature fusion strategy based on the average difference of modes is proposed to enhance the distinctive expression of different regional features in the source image. Secondly, a lightweight face recognition network MobileFaceNet-Coo and an adaptive recognition strategy based on edge-cloud collaboration are proposed in order to solve the problem of large and slow fusion recognition network models in practical applications. This strategy selects the recognition model through image quality and effectively utilizes hardware resources. Experimental results demonstrate that the recognition rate of fused infrared light is 13.96 percentage point higher than that of visible light alone. Real-world project result shows that this method significantly improves real-time and accuracy of face recognition in complex environments.