Abstract
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-The-Art approaches.
Original language | English |
---|---|
Title of host publication | 2019 IEEE International Conference on Visual Communications and Image Processing (VCIP 2019) |
Editors | Mark Pickering, Qiang Wu, Lei Wang, Jiaying Liu |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Number of pages | 4 |
ISBN (Electronic) | 9781728137230 |
ISBN (Print) | 9781728137247 |
DOIs | |
Publication status | Published - 2019 |
Event | IEEE Visual Communications and Image Processing 2019 - Sydney, Australia Duration: 1 Dec 2019 → 4 Dec 2019 Conference number: 34th http://www.vcip2019.org/ |
Conference
Conference | IEEE Visual Communications and Image Processing 2019 |
---|---|
Abbreviated title | VCIP 2019 |
Country | Australia |
City | Sydney |
Period | 1/12/19 → 4/12/19 |
Internet address |
Keywords
- deep learning
- facial expression recognition
- facial-motion mask
- prior knowledge