Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

Abstract

The exploration of facial emotion recognition aims to analyze psychological characteristics of juveniles involved in crimes and promote the application of deep learning to psychological feature extraction. First, the relationship between facial emotion recognition and psychological characteristics is discussed. On this basis, a facial emotion recognition model is constructed by increasing the layers of the convolutional neural network (CNN) and integrating CNN with several neural networks such as VGGNet, AlexNet, and LeNet-5. Second, based on the feature fusion, an optimized Central Local Binary Pattern (CLBP) algorithm is introduced into the CNN to construct a CNN-CLBP algorithm for facial emotion recognition. Finally, the validity analysis is conducted on the algorithm after the preprocessing of face images and the optimization of relevant parameters. Compared with other methods, the CNN-CLBP algorithm has higher accuracy in facial expression recognition, with an average recognition rate of 88.16%. Besides, the recognition accuracy of this algorithm is improved by image preprocessing and parameter optimization, and there is no poor-fitting. Moreover, the CNN-CLBP algorithm can recognize 97% of the happy expressions and surprised expressions, but the misidentification rate of sad expressions is 22.54%. The research result provides data reference and direction for analyzing psychological characteristics of juveniles involved in crimes.