TY - GEN
T1 - Disentangled Feature Based Adversarial Learning for Facial Expression Recognition
AU - Bai, Mengchao
AU - Xie, Weicheng
AU - Shen, Linlin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - A facial expression image can be considered as an addition of expressive component to a neutral expression face. With this in mind, in this paper, we propose a novel end-to-end adversarial disentangled feature learning (ADFL) framework for facial expression recognition. The ADFL framework is mainly composed of three branches: expression disentangling branch ADFL-d, neutral expression branch ADFL-n and residual expression branch ADFL-r. The ADFL-d and ADFL-n aim to extract the expressive component and neutral component, respectively. The ADFL-r extracts the residual expression by calculating the difference between feature maps of ADFL-d and ADFL-n, and uses the residual expression feature for expression classification. Experimental results on several benchmark databases (CK+, MMI and Oulu-CASIA) show that the proposed method has remarkable performance compared to state-of-the-art methods.
AB - A facial expression image can be considered as an addition of expressive component to a neutral expression face. With this in mind, in this paper, we propose a novel end-to-end adversarial disentangled feature learning (ADFL) framework for facial expression recognition. The ADFL framework is mainly composed of three branches: expression disentangling branch ADFL-d, neutral expression branch ADFL-n and residual expression branch ADFL-r. The ADFL-d and ADFL-n aim to extract the expressive component and neutral component, respectively. The ADFL-r extracts the residual expression by calculating the difference between feature maps of ADFL-d and ADFL-n, and uses the residual expression feature for expression classification. Experimental results on several benchmark databases (CK+, MMI and Oulu-CASIA) show that the proposed method has remarkable performance compared to state-of-the-art methods.
KW - Disentangled feature
KW - adversarial learning
KW - expression disentangling
KW - residual expression
UR - http://www.scopus.com/inward/record.url?scp=85076823426&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8802941
DO - 10.1109/ICIP.2019.8802941
M3 - Conference contribution
AN - SCOPUS:85076823426
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 31
EP - 35
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -