Face recognition algorithm based on improved neural network

In complex environment, the performance of traditional face recognition algorithm decreases greatly. In order to further improve the recognition accuracy of current face recognition algorithms, this paper proposes two face recognition algorithms based on improved convolutional neural networks through the analysis of the defects of traditional algorithms. Finally, we will build a new face recognition model to verify the effectiveness of the two new methods. The first method is to extract and classify face features by fusing convolution layer and pooling layer, train neural network by stochastic gradient descent method, recognize face by Softmax classifier, and finally solve the over-fitting problem by "Dropout" method. The second method is to use the network link structure of bisymmetric LetNet and DCT-LBP joint processing method to process the input image. The two algorithms have some similarities, and both can improve the accuracy of face recognition.