Occluded Face Recognition Network Based on Deep Feature Suppression
Facial recognition technology is a key technology for verifying personal evidence in public security investigations.Although existing algorithms can achieve high recognition accuracy in unobstructed face recognition,effective facial features are lost when a face is occluded,resulting in a significant decrease in recognition accuracy.Hence,an occluded face recognition network based on deep feature suppression is proposed to address these issues.The network adaptively generates feature masks based on occluded faces,suppresses features damaged by occlusion in deep feature maps through feature masks,and uses the suppressed features to complete face recognition.To improve the discrimination of suppressed features,a twin network structure is used in the training phase to measure and learn the depth features of the occluded and corresponding unobstructed faces.Simultaneously,to fully utilize different levels of feature information,a Feature Pyramid Network(FPN)and an adaptive feature fusion module are constructed to extract multiscale feature information from faces.The feature layers containing more feature information are assigned greater fusion weights,thereby enhancing the representation abilities of the features.The experimental results show that the proposed method has good robustness,with accuracy rates of 99.50％and 98.42％for the LFW and LFW mask occlusions in the dataset,respectively,and 100％,100％,99.86％,and 99.02％for the four experimental settings in the AR dataset,respectively,surpassing those of current mainstream algorithms.
face recognitionoccluded face recognitionadaptive feature integrationfeature maskmetric learning
万方数据
维普
