Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
This work addresses the problem of improving facial expression analysis for applications like human-computer interaction, though it is incremental as it builds on existing multi-task learning approaches.
The paper tackles the joint tasks of facial action unit detection and face alignment by proposing an end-to-end deep learning framework with an adaptive attention module, achieving state-of-the-art performance on BP4D and DISFA benchmarks.
Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.