Research on Face Recognition Based on Gabor-LeNet Convolutional Neural Network Model

Abstract

Abstract Aiming at the problems of slowing convergence speed, and low recognition accuracy in the face recognition training of the existing LeNet-5 convolutional neural network, an improved LeNet-5 convolutional neural network model is proposed and applied for face recognition. The main improvement is the use of Gabor filter to initialize the first convolutional layer and the activation function of Parametric Rectified Linear Unit (PReLU). The recognition accuracy of the improved model on ORL and GT face datasets has reached more than 98%. At the same time, the recognition accuracy rate on the face data set AR with occlusion has reached more than 90%, indicating that the improved model has strong robustness.