Biblio

In the past few years, there has been increasing interest in the perception of human expressions and mental states by machines, and Facial Expression Recognition (FER) has attracted increasing attention. Facial Action Unit (AU) is an early proposed method to describe facial muscle movements, which can effectively reflect the changes in people's facial expressions. In this paper, we propose a high-performance facial expression recognition method based on facial action unit, which can run on low-configuration computer and realize video and real-time camera FER. Our method is mainly divided into two parts. In the first part, 68 facial landmarks and image Histograms of Oriented Gradients (HOG) are obtained, and the feature values of action units are calculated accordingly. The second part uses three classification methods to realize the mapping from AUs to FER. We have conducted many experiments on the popular human FER benchmark datasets (CK+ and Oulu CASIA) to demonstrate the effectiveness of our method.

Facial expression is the most powerful and natural non-verbal emotional communication method. Facial Expression Recognition(FER) has significance in machine learning tasks. Deep Learning models perform well in FER tasks, but it doesn't provide any justification for its decisions. Based on the hypothesis that facial expression is a combination of facial muscle movements, we find that Facial Action Coding Units(AUs) and Emotion label have a relationship in CK+ Dataset. In this paper, we propose a model which utilises AUs to explain Convolutional Neural Network(CNN) model's classification results. The CNN model is trained with CK+ Dataset and classifies emotion based on extracted features. Explanation model classifies the multiple AUs with the extracted features and emotion classes from the CNN model. Our experiment shows that with only features and emotion classes obtained from the CNN model, Explanation model generates AUs very well.