Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit

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 h...

Full description

Saved in:
Bibliographic Details
Main Authors: Kim, Sunbin, Kim, Hyeoncheol
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Deep learning
Explanation Model
Face recognition
Facial Action Coding System
Facial Expression Recognition
Feature extraction
Gold
Hidden Markov models
Justification
Task analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2375-9356
DOI:10.1109/BIGCOMP.2019.8679370