Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Abstract

In order to further improve the ability of automatic feature extraction of deep learning technology and better solve the problem of system recognition ability declining when the number of training samples is reduced or the number of iterations is reduced, this paper proposes a fisher-based convolutional neural network algorithm, which automatically obtains the structural feature information of a face image with the help of a deep learning algorithm network and then reduces the number of weights with the help of a convolutional neural network to further reduce the complexity of the face recognition model. Through the system test, the verification data shows that the recognition error rate can be controlled at about 10% by extracting 10 of the 14 pictures of each type of face for training. When the training images are reduced, the recognition rate can be further improved.