Visible to Near-Infrared Face Image Conversion Technology Base on Generative Adversarial Nets
Infrared image can capture face information more clearly in the case of poor light conditions.It is often found that cross do-main recognition occurs in practical scenarios,this paper proposed an improved cross domain of visible to near-infrared face image conversion technology based on antagonism generation network.The generator component of the cyclical adversarial generative network has been refined by adding a facial classification branch and incorporating facial discrimination attribute constraints,enabling the gen-erated images to effectively preserve facial identification details.Additionally,the integration of a non-local algorithm allows the model to automatically focus on key facial features,enhancing the quality of cross-domain face generation.To ensure effective mapping during training,a cyclic consistency loss is proposed,particularly for larger networks.Experimental results on public datasets demonstrate im-proved performance of the proposed approach.
万方数据
维普
