Abstract
Automatically recognizing facial expression is an important part for human-machine interaction. In this paper, we first review the previous studies on both 2D and 3D facial expression recognition, and then summarize the key research questions to solve in the future. Finally, we propose a 3D facial expression recognition (FER) algorithm using convolutional neural networks (CNNs) and landmark features/masks, which is invariant to pose and illumination variations due to the solely use of 3D geometric facial models without any texture information. The proposed method has been tested on two public 3D facial expression databases: BU-4DFE and BU-3DFE. The results show that the CNN model benefits from the masking, and the combination of landmark and CNN features can further improve the 3D FER accuracy.
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