Research On Face Verification Algorithm Based On Deep Learning

Face verification,as a branch of face recognition,is of great significance in maintaining social stability and personal safety.At present,face verification methods mainly include:traditional pattern recognition methods represented by shallow models such as support vector machines,k-nearest neighbors,and decision trees,and deep learning methods represented by convolutional neural networks.The recognition accuracy of traditional pattern recognition methods depends on the results of manual extraction of features,which is easily affected by external conditions and has a low degree of intelligence.Deep learning methods can extract the hidden features of human faces from complex data in an autonomous learning manner,and have strong generalization ability.Therefore,this paper proposes two face verification algorithms based on deep learning.The specific work is as follows:(1)Aiming at the problem that the existing deep learning face verification method has high data labeling cost and poor model training effect on the data set with few training samples,a face verification algorithm combining LeNet-5 and Siamese neural network is proposed.First,the face data is matched as a pair of samples and sent to the network.The Siamese neural network framework is used to construct a two-branch LeNet-5 convolution network for face feature extraction.By reducing the convolution kernel,increasing the convolution layers,and changing the activation function adjusts the model structure;Then,the contrastive loss function is used for network optimization to minimize intra-class differences and maximize inter-class differences,and improve the network's ability to distinguish samples;Finally,the sample category is judged by measuring the similarity between sample features.This method combines deep learning and metric learning,which not only avoids complex artificial feature extraction,but also simplifies the face verification process.Experimental results show that the recognition accuracy of this method on face data sets with fewer training samples is significantly improved.(2)In order to solve the problem that the feature expression ability and recognition accuracy of the fusion LeNet-5 and Siamese networks are not strong in the face data sets with great changes in facial expression and illumination,a fusion of LeNet-Residual and Siamese based on residual structure is proposed.First,before the data matching,the images are preprocessed with local texture feature enhancement,and the adverse effects of overexposure or shadow on the image are removed through gamma correction,DoG filtering and contrast equalization;Then,it is introduced on the basis of the fusion network two residual units,A and B,design a two-branch LeNet-Residual convolutional neural network to extract richer facial features,and use contrastive loss to optimize the network;Finally,measure the similarity between sample features to determine the sample category.The experimental results show that the method has stronger feature expression ability and higher classification accuracy on the face data set with large changes in facial expressions and lighting.