3D Facial Expression Recognition Using Multi-channel Deep Learning Framework

Facial expression offers an important way of detecting the affective state of a human being. It plays a major role in various fields such as the estimation of students' attention level in online education, intelligent transportation systems and interactive games. This paper proposes a facial expression recognition system in which two channels of featured images are used to represent a 3D facial scan. Features are extracted from the local binary pattern and local directional pattern using a fine-tuned pre-trained AlexNet and a shallow convolutional neural network. The feature sets are then fused together using canonical correlation analysis. The fused feature set is fed into a multi-support vector machine (mSVM) classifier to classify the expressions into seven basic categories: anger, disgust, fear, happiness, neutral, sadness and surprise. Experiments were carried out on the Bosphorus database using tenfold cross-validation with mutually exclusive training and testing samples. The results show an average accuracy of 87.69% using an mSVM classifier with a polynomial kernel and demonstrate that the system performs better by characterizing the peculiarities in facial expressions than alternative state-of-the-art approaches.