VGG16 based on dilated convolution for face recognition

Abstract

Face recognition has wide applications in fields such as security systems, biometrics, and human-computer interaction. However, traditional face recognition methods face challenges in capturing details and reducing model complexity. To address these issues, this paper proposes a new method based on VGG16, which improves recognition accuracy and reduces parameter quantity by introducing dilated convolution and parameter pruning. First, the hole convolution is introduced to expand the Receptive field and capture more details to enhance the ability of the model in distinguishing facial features. Next, parameter pruning is applied to reduce redundant parameters, optimize model structure, and improve computational efficiency. This article conducted experimental evaluation on the classic face recognition dataset CK+ dataset. The results show that the proposed method is significantly superior to the traditional VGG16 model in terms of recognition accuracy. At the same time, the use of pruning technology significantly reduces the number of parameters in the model and improves computational efficiency. The experimental outcomes conclusively validate the effectiveness and feasibility of the proposed method.