| Title | Facial Expression Intensity Estimation Considering Change Characteristic of Facial Feature Values for Each Facial Expression |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Shiomi, Takanori, Nomiya, Hiroki, Hochin, Teruhisa |
| Conference Name | 2022 23rd ACIS International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Summer) |
| Date Published | jul |
| Keywords | character recognition, emotion recognition, Emotion Space Regression, Estimation, face recognition, facial expression recognition, Facial Image Sequence, facial recognition, Human Behavior, image recognition, image sequences, machine learning, Metrics, pubcrawl, resilience, Resiliency |
| Abstract | Facial expression intensity, which quantifies the degree of facial expression, has been proposed. It is calculated based on how much facial feature values change compared to an expressionless face. The estimation has two aspects. One is to classify facial expressions, and the other is to estimate their intensity. However, it is difficult to do them at the same time. There- fore, in this work, the estimation of intensity and the classification of expression are separated. We suggest an explicit method and an implicit method. In the explicit one, a classifier determines which types of expression the inputs are, and each regressor determines its intensity. On the other hand, in the implicit one, we give zero values or non-zero values to regressors for each type of facial expression as ground truth, depending on whether or not an input image is the correct facial expression. We evaluated the two methods and, as a result, found that they are effective for facial expression recognition. |
| DOI | 10.1109/SNPD-Summer57817.2022.00012 |
| Citation Key | shiomi_facial_2022 |
Facial Expression Intensity Estimation Considering Change Characteristic of Facial Feature Values for Each Facial Expression