A happiness emotion detection method based on deep learning

Abstract

Some researches show that learner’s Emotional state has an important impact on affective and cognitive processes influencing learning. A positive emotional state can enhance learning outcome. It is important to detect learner’s emotion state in learning processes unconsciously. Generally, emotions can be classified within the two dimensions, valence and activation. Happiness is an activating positive valence emotional state. This paper presents a happiness emotion detection method based on deep learning. Firstly, a certain amount of face images which include static emotion are selected from the image database. Faces are detected by using a face detector and aligned by using eye locations. Then, the face images are clipped into proper size to match the convolutional neural network input. In our classifier, input layer accepts single channel to process grayscale images, and the output layer outputs two classes, i.e. happiness emotion and non-happiness. Fourfold cross-validation is performed on the facial expression image dataset which is divided into four subsets randomly. In every round of cross validation, one subset is used for testing and other three subsets are used for training. The experiment results show that the average accuracy is up to 98.78 percent which is enough to use in learning outcome evaluation.