Abstract
In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.
Highlights
Facial expression recognition plays a great role in human–computer interaction
Most Convolutional neural network (CNN) -based facial recognition tasks use the entire face image as the input information, but what we found in our observations is that the judgment of facial expression is usually completed based on the information of several sensitive components in some areas of the face, such as eye, nose, and mouth
Facial expression analysis is an interesting and challenging task, and it has been applied in many fields such as human–computer interaction and remote education
Summary
Facial expression recognition plays a great role in human–computer interaction. In the course of human communication, 55% of the information is conveyed by different facial expressions, voice constitutes 38% of a communicated message, and language only constitutes 7% [1], facial expression recognition has attracted much attention in recent years [2,3], and has many important applications in, e.g., remote education, safety, medicine, psychology and human–robot interaction systems. Convolutional neural network (CNN) [19] is a very effective method to recognize facial emotions They can perform the feature extraction and classification process simultaneously, and can automatically discover the multiple levels of representations in data. Since Convolutional Neural Network (CNN) has already proved its excellence in many image recognition tasks, we expect that it can show better results than already existing methods in facial expression prediction problems. To solve the problems caused by too few data, this paper divides some organ images that have important contributions to facial expression recognition from the raw images, which can improve the quantity of datasets, and improve the quality of extracted information Since it is difficult for the CNN model based on single task to improve the overall accuracy rate in the recognition task, this paper proposes a multi-task learning-based recognition model, which can modify the expression features extracted from the raw images with the help of the auxiliary model, so that the final extracted feature information is more in line with the ideal expression feature information.
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