Algorithm Analysis of Face Recognition Robot Based on Deep Learning

Abstract

With the continuous improvement of computer software and hardware performance, a large amount of image and video data can be easily collected and quickly transmitted, and new recognition methods that introduce deep learning are emerging, making the application and research of face recognition technology. The value is also increasingly prominent. The purpose of this paper is to study the face recognition robot implementation algorithm based on deep learning. The research background and significance of face recognition and expression recognition, which are the core of facial biological information extraction, are introduced. The face feature extraction network structure of Inception-ResNet-V1 has been improved, and high recognition features of faces can be obtained. At the same time, the training of the feature extraction model of the self-built training set and the adjustment of hyperparameters are completed. Finally, the effectiveness of the improved network in this paper is fully verified in the LFW test set and the actual robot environment. It is verified by experiments that the proposed optimization method can improve the performance of the network. It also verified the significant research significance of the current deep learning direction through practice.