Deep learning has been applied to achieve significant progress in emotion recognition. Despite such substantial progress, existing approaches are still hindered by insufficient training data, and the resulting models do not generalize well under mismatched conditions. To address this challenge, we propose a learning strategy which jointly transfers the knowledge learned from rich datasets to source-poor datasets. Our method is also able to learn cross-domain features which lead to improved recognition performance. To demonstrate the robustness of our proposed framework, we conducted experiments on three benchmark emotion datasets including eNTERFACE, SAVEE, and EMODB. Experimental results show that the proposed method surpassed state-of-the-art transfer learning schemes by a significant margin.
@article{arxiv.2003.11136,
title = {Joint Deep Cross-Domain Transfer Learning for Emotion Recognition},
author = {Dung Nguyen and Sridha Sridharan and Duc Thanh Nguyen and Simon Denman and Son N. Tran and Rui Zeng and Clinton Fookes},
journal= {arXiv preprint arXiv:2003.11136},
year = {2020}
}