Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

Abstract

Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar cascade classifier to extract facial features and utilized convolutional neural networks (CNNs) as the backbone model to achieve an efficient system. The proposed approach achieved an accuracy of approximately 70% on the FER-2013 dataset in the experiment. This result represents an improvement of 7.83% compared to that of the baseline system. This significant enhancement improves the accuracy of facial expression recognition. Herein, the proposed approach also extended to multiple face expression recognition; the module was further experimented with and obtained promising results. The outcomes of this research will establish a solid foundation for real-time monitoring and prevention of conditions such as depression through an emotion alert system.