Visible to the public Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit

TitleDeep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit
Publication TypeConference Paper
Year of Publication2019
AuthorsKim, Sunbin, Kim, Hyeoncheol
Conference Name2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
KeywordsCK+ Dataset, CNN model, Computational modeling, convolutional neural nets, convolutional neural network model, deep explanation model, Deep Learning, deep learning models, emotion classes, emotion recognition, Explanation Model, face recognition, Facial Action Coding System, facial action coding unit, facial action coding units, facial expression recognition, facial muscle movements, facial recognition, feature extraction, FER tasks, Gold, Hidden Markov models, Human Behavior, Justification, learning (artificial intelligence), machine learning tasks, Metrics, nonverbal emotional communication method, pubcrawl, Resiliency, Task Analysis
AbstractFacial 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.
Citation Keykim_deep_2019