Abstract
In the facial expression recognition field, RGB image-involved models have always achieved the best performance. Since RGB images are easily influenced by illumination, skin color, and cross-databases, the effect of these methods decreases accordingly. To avoid these issues, we propose a novel facial expression recognition framework in which the input only relies on a single depth image since depth image performs very stable in cross-situations. In our framework, we pretrain an RGB face image synthesis model by a generative adversarial network (GAN) using a public database. This pretrained model can synthesize an RGB face image under a unified imaging situation from a depth face image input. Then, introducing the attention mechanism based on facial landmarks into a convolutional neural network (CNN) for recognition, this attention mechanism can strengthen the weights of the key parts. Thus, our framework has a stable input (depth face image) while retaining the natural merits of RGB face images for recognition. Experiments conducted on public databases demonstrate that the recognition rate of our framework is better than that of the state-of-the-art methods, which are also based on depth images.
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Cai, J., Xie, H., Li, J., Li, S. (2020). Facial Expression Recognition with an Attention Network Using a Single Depth Image. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_25
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