Facial Emotion Recognition Using Light Field Images with Deep Attention-Based Bidirectional LSTM

Alireza Sepas-Moghaddam, S. Ali Etemad, Fernando Pereira, Paulo Lobato Correia

Abstract: Light field cameras are able to capture the intensity of light rays coming from multiple directions, thus representing the visual scene from multiple viewpoints. This paper exploits the rich spatio-angular information available in light field images for facial emotion recognition. In this context, a new deep network is proposed that first extracts spatial features using a VGG16 convolutional neural network. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent neural network is used to learn spatio-angular features from viewpoint feature sequences, exploring both forward and backward angular relationships. Additionally, an attention mechanism allows our model to selectively focus on the most important spatio-angular features, thus enabling a more effective learning outcome. Finally, a fusion scheme is adopted to obtain the emotion recognition classification results. Comprehensive experiments have been conducted on the IST-EURECOM Light Field Face database using two challenging evaluation protocols, showing the superiority of our method over the state-of-the-art.