Face Recognition and Model Analysis Using CNN Based on Self-Collected Information

Abstract

Face recognition helps people to determine the features and characteristics of a face, which in turn helps the police to find criminals, reduce the crime rate and ensure the safety of the society. In order to further improve the performance of face recognition, the main objective of this research is to collect a large amount of face information using Convolutional Neural Networks (CNN) to achieve effective recognition. Specifically, first, a code is used in the annotation to define the coordinates of the face. This function converts the classification labels into numbers that can be understood by the model. Second, generate targets in order to parse the annotation file and obtain objects from it. Third, a CNN model is constructed. The features in the data are captured using the adaptive weights of the CNN. In addition, the predictive performance of different models is analysed and compared. In addition, the objective function will transform the face coordinates of the file attached to the boxed list, which helps to acquire the commanded faces in the image file. It is shown through experiments that the proposed model can perform face recognition effectively. The proposed program in this paper can provide valuable thoughts and insights to the field.