关键词: 人脸验证, 深度学习, Siamese网络

Abstract:

Face-based identity verification is very important for personal security and social stability. The traditional face verification method relies on manual construction of visual features and is susceptible to external conditions, so the accuracy of face recognition is not high in real scenes. The deep learning model extracts features in autonomous learning and can extract the hidden features of the face in complex data. However, the most deep learning face verification methods require a large number of training samples with identity tags, which additionally increases the cost of tagging data. To solve these problems, this paper proposes a face verification method based on LeNet-5 and Siamese neural network. Based on the framework of Siamese neural network, this algorithm introduces LeNet-5 convolution neural network. It extends the single-branch LeNet-5 network to a double-branch LeNet-5 network with the same structure and shared parameters. It adjusts the network structure by reducing the convolution kernel and increasing the number of convolution layers. It uses Contrastive Loss function to train fusion network. Experimental results show that this algorithm can achieve high recognition accuracy on different face data sets.

Key words: face verification, deep learning, Siamese network