A Face Recognition Algorithm Based on Improved Resnet

Abstract

Regarding the problem that the increasing number of layers of CNN (convolutional neural network) leads to the decline of accuracy, an improved loss function algorithm based on the Resnet-50 model is proposed. The Softmax loss function lacks constraints on the distance within the same class and between different classes. Replacing the Softmax layer with improved Arcface loss enables the neural network to learn more distinguishing features. Experiments on LFW and AgeDB data sets show that the algorithm can not only learn deep-face characteristics but also efficiently improve the accuracy of face recognition compared with ordinary CNN. In the meantime, the improved Resnet also obtains a higher discerning rate under the conditions of occlusions, illumination, expression, Age.