Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations

Oyebade K. Oyedotun, Girum Demisse, Abd El Rahman Shabayek, Djamila Aouada, Bjorn Ottersten; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3161-3168

Humans use facial expressions successfully for conveying their emotional states. However, replicating such success in the human-computer interaction domain is an active research problem. In this paper, we propose deep convolutional neural network (DCNN) for joint learning of robust facial expression features from fused RGB and depth map latent representations. We posit that learning jointly from both modalities result in a more robust classifier for facial expression recognition (FER) as opposed to learning from either of the modalities independently. Particularly, we construct a learning pipeline that allows us to learn several hierarchical levels of feature representations and then perform the fusion of RGB and depth map latent representations for joint learning of facial expressions. Our experimental results on the BU-3DFE dataset validate the proposed fusion approach, as a model learned from the joint modalities outperforms models learned from either of the modalities.