Facial Expression Recognition Using a Temporal Ensemble of Multi-Level Convolutional Neural Networks

Emotion recognition is indispensable in human-machine interaction systems. It comprises locating facial regions of interest in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Despite several breakthroughs in image classification, particularly in facial expression recognition, this research area is still challenging, as sampling in the wild is a demanding task. In this study, a two-stage method is proposed for recognizing facial expressions given a sequence of images. At the first stage, all face regions are extracted in each frame, and essential information that would be helpful and related to human emotion is obtained. Then, the extracted features from the previous step are considered temporal data and are assigned to one of the seven basic emotions. In addition, a study of multi-level features is conducted in a convolutional neural network for facial expression recognition. Moreover, various network connections are introduced to improve the classification task. By combining the proposed network connections, superior results are obtained compared to state-of-the-art methods on the FER2013 dataset. Furthermore, the performance of our temporal model is better than that of the single architecture of the 2017 EmotiW challenge winner on the AFEW 7.0 dataset.