Multi-Modality Empowered Network for Facial Action Unit Detection

Peng Liu, Zheng Zhang, Huiyuan Yang, Lijun Yin

Abstract: This paper presents a new thermal empowered multi-task network (TEMT-Net) to improve facial action unit detection. Our primary goal is to leverage the situation that the training set has multi-modality data while the application scenario only has one modality. Thermal images are robust to illumination and face color. In the proposed multi-task framework, we utilize both modality data. Action unit detection and facial landmark detection are correlated tasks. To utilize the advantage and the correlation of different modalities and different tasks, we propose a novel thermal empowered multi-task deep neural network learning approach for action unit detection, facial landmark detection and thermal image reconstruction simultaneously. The thermal image generator and facial landmark detection provide regularization on the learned features with shared factors as the input color images. Extensive experiments are conducted on the BP4D and MMSE databases, with the comparison to the state of the art methods. The experiments show that the multi-modality framework improves the AU detection significantly.