Facial Emotion Recognition Using 3D Face Reconstruction

In recent days, autonomous driving systems (ADS) effectively utilize facial emotion recognition (FER) results for safe driving. In FER, the system provides the user emotions such as happy, sad, anger, surprise, disgust, fear, or neutral. These emotions provide helpful information for safe driving and reduce the chances of road accidents. The conventional FER approaches use 2D images as their inputs and classify the user emotions. However, the 2D face images in the conventional FER approaches have limited features for model training. In addition, the features from the 2D face images themselves are not sufficient for accurate emotion classification. To reduce the feature extraction issues in the conventional FER approaches, we propose a 3D face image-based FER approach that uses the 3D face reconstruction technique for converting the 2D face images into 3D face images. The deep convolutional neural networks (DCNNs) used in the proposed FER approach efficiently use the 3D face images as inputs and classify the user emotions with minimum errors. The experiment results show that the proposed 3D face image-based FER approach achieves 99% classification accuracy which is better than the conventional 2D face image-based FER approach.