Action Unit Assisted Facial Expression Recognition

摘要

Facial expression recognition is vital to many intelligent applications such as human-computer interaction and social networks. For machines, learning to classify six basic human expressions (anger, disgust, fear, happiness, sadness and surprise) is still a big challenge. This paper proposed a convolutional neural network based on AlexNet combining a Bayesian network. Besides traditional features, the relationships between facial action units (AU) and expressions are captured. Firstly, a convolutional neural network to extract features from images is constructed. Then, a Bayesian network is established to learn the dependencies of AUs and expressions from joint probabilities and conditional probabilities. Finally, ensemble learning is used to combine the features of expressions, AUs and dependencies between the two. Our experiments on popular datasets show that the proposed method performs well corn-pared with latest approaches.