Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

Abstract

With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition.