A Compact Deep Learning Model for Robust Facial Expression Recognition

In this paper, we propose a compact frame-based facial expression recognition framework for facial expression recognition which achieves very competitive performance with respect to state-of-the-art methods while using much less parameters. The proposed framework is extended to a frame-to-sequence approach by exploiting temporal information with gated recurrent units. In addition, we develop an illumination augmentation scheme to alleviate the over-fitting problem when training the deep networks with hybrid data sources. Finally, we demonstrate the performance improvement by using the proposed technique on some public datasets.