Emotion recognition from facial expression using deep convolutional neural network

Abstract

Automatic facial expression recognition is an actively emerging research in Emotion Recognition. This paper extends the deep Convolutional Neural Network (CNN) approach to facial expression recognition task. This task is done by detecting the occurrence of facial Action Units (AUs) as a subpart of Facial Action Coding System (FACS) which represents human emotion. In the CNN fully-connected layers we employ a regularization method called "dropout" that proved to be very effective to reduce overfitting. This research uses the extended Cohn Kanade (CK+) dataset which is collected for facial expression recognition experiment. The system performance gain average accuracy rate of 92.81%. The system has been successfully classified eight basic emotion classes. Thus, the proposed method is proven to be effective for emotion recognition.