Hwooi, Stephen Khor Wen and Loo, Chu Kiong and Sabri, Aznul Qalid Md (2020) Emotion differentiation based on arousal intensity estimation from facial expressions. In: iCatse International Conference on Information Science and Applications (ICISA), 16-18 December 2019, Seoul, South Korea.
Full text not available from this repository.Abstract
Emotion recognition still remains a research issue that can be improved significantly. This paper worked on it using facial feature extraction followed by arousal level estimation. We extracted facial features via four Convolutional Neural Networks (CNNs) pre-trained on ImageNet: ResNet152V2, Xception, ResNeXt101, and InceptionResNetV2 based on low memory usage and modernity. For estimating arousal levels, Exponential Linear Unit (ELU) was compared with Rectified Linear Unit (ReLU) in a custom deep model framework. Wild facial expression image data was taken from the relatively new AffectNet database. Up to this point, recognition of emotions from AffectNet is largely treated as a classification problem, with arousal level estimation mostly unexplored. Our results showed that the custom deep model trained on multiple classes and fine-tuned on one class was more effective than that trained and tested on the same class. ResNet152V2 displayed the best performance among the CNNs, emphasizing its suitability for the CultureNet-based architecture from which this algorithm is based. Further research includes more activation functions, ResNet variants, and the application of a softmax layer.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | iCatse International Conference on Information Science and Applications (ICISA), Seoul, South Korea, Dec 16-18, 2019 |
| Uncontrolled Keywords: | Facial expressions; Convolutional neural networks; Continuous dimensional space |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 13 Jun 2023 07:45 |
| Last Modified: | 13 Jun 2023 07:45 |
| URI: | http://eprints.um.edu.my/id/eprint/37034 |
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