TY - GEN
T1 - Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit
AU - Kim, Sunbin
AU - Kim, Hyeoncheol
N1 - Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2017R1A2B4003558)
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Facial expression is the most powerful and natural non-verbal emotional communication method. Facial Expression Recognition(FER) has significance in machine learning tasks. Deep Learning models perform well in FER tasks, but it doesn't provide any justification for its decisions. Based on the hypothesis that facial expression is a combination of facial muscle movements, we find that Facial Action Coding Units(AUs) and Emotion label have a relationship in CK+ Dataset. In this paper, we propose a model which utilises AUs to explain Convolutional Neural Network(CNN) model's classification results. The CNN model is trained with CK+ Dataset and classifies emotion based on extracted features. Explanation model classifies the multiple AUs with the extracted features and emotion classes from the CNN model. Our experiment shows that with only features and emotion classes obtained from the CNN model, Explanation model generates AUs very well.
AB - Facial expression is the most powerful and natural non-verbal emotional communication method. Facial Expression Recognition(FER) has significance in machine learning tasks. Deep Learning models perform well in FER tasks, but it doesn't provide any justification for its decisions. Based on the hypothesis that facial expression is a combination of facial muscle movements, we find that Facial Action Coding Units(AUs) and Emotion label have a relationship in CK+ Dataset. In this paper, we propose a model which utilises AUs to explain Convolutional Neural Network(CNN) model's classification results. The CNN model is trained with CK+ Dataset and classifies emotion based on extracted features. Explanation model classifies the multiple AUs with the extracted features and emotion classes from the CNN model. Our experiment shows that with only features and emotion classes obtained from the CNN model, Explanation model generates AUs very well.
KW - Deep learning
KW - Explanation Model
KW - Facial Action Coding System
KW - Facial Expression Recognition
KW - Justification
UR - http://www.scopus.com/inward/record.url?scp=85064647701&partnerID=8YFLogxK
U2 - 10.1109/BIGCOMP.2019.8679370
DO - 10.1109/BIGCOMP.2019.8679370
M3 - Conference contribution
AN - SCOPUS:85064647701
T3 - 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
BT - 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
Y2 - 27 February 2019 through 2 March 2019
ER -