TY - GEN
T1 - Hand-crafted feature guided deep learning for facial expression recognition
AU - Zeng, Guohang
AU - Zhou, Jiancan
AU - Jia, Xi
AU - Xie, Weicheng
AU - Shen, Linlin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/5
Y1 - 2018/6/5
N2 - A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.
AB - A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.
KW - Deep metric learning
KW - Facial expression recognition
KW - Feature loss
KW - Hand crafted feature
UR - http://www.scopus.com/inward/record.url?scp=85049414285&partnerID=8YFLogxK
U2 - 10.1109/FG.2018.00068
DO - 10.1109/FG.2018.00068
M3 - Conference contribution
AN - SCOPUS:85049414285
T3 - Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
SP - 423
EP - 430
BT - Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Y2 - 15 May 2018 through 19 May 2018
ER -