Face Recognition Method for Enterprise Workstations Based on Convolutional Neural Network Optimization Algorithm

Abstract

Nowadays, the world economy is developing rapidly, new Internet companies are emerging. Based on the consideration of effectively improving the working efficiency of employees, enhance the competitiveness of enterprises, and facilitate managers to grasp the working status of employees at any time, this paper proposed a face recognition method for enterprise positions based on a convolutional neural network (CNN) optimization algorithm. At first, this paper established enterprise employee face classification model based on the TensorFlow deep learning framework, then used convolutional neural network to extract employee face image features, and introduced Keras deep learning library to train face recognition model, finally used TensorFlow-supported momentum gradient descent optimization method to effectively optimize the CNN model and used the loss function to effectively evaluate the performance of the model, thereby effectively improving the recognition accuracy of the face recognition algorithm. The algorithm proposed in this paper is used to identify the working status of employees in practice. The validity of the algorithm is verified by questionnaire results, and compared with typical face recognition algorithms. The experiment results clearly show that to some extent the method we proposed has higher recognition accuracy and better practicality, which will help companies find out the working status of their employees.