Fadhil Yusuf Rahadika1, Fadhil Yusuf Rahadika1 and Novanto Yudistira, Novanto Yudistira and Yuita Arum Sari, Yuita Arum Sari (2021) Facial Expression Recognition using Residual Convnet with Image Augmentations. Jurnal Ilmu Komputer dan Informasi, 14 (2). pp. 127-135. ISSN e-ISSN 2502-9274
![[thumbnail of Facial Expression Recognition using Residual Convnet with Image Augmentations.pdf]](http://eprints.triatmamulya.ac.id/style/images/fileicons/text.png) |
Text
Facial Expression Recognition using Residual Convnet with Image Augmentations.pdf - Other Download (2MB) |
Abstract
During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings
to prevent the spread of the COVID-19 virus. In the online video meeting, some micro-interactions are
missing when compared to direct social interactions. The use of machines to assist facial expression
recognition in online video meetings is expected to increase understanding of the interactions among
users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image
classification. In this study, some open facial expression datasets were used to train CNN-based neural
networks with a total number of training data of 342,497 images. This study gets the best results using
ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture
is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch
size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972,
FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From
this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning,
and there is a potential for better performance with the pre-training model. The code is available at
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | facial expression recognition, CNN, ResNet, Mish, Accuracy Booster Plus |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Mrs Ni Made Yunia Dwi Savitri |
| Date Deposited: | 18 Nov 2022 07:26 |
| Last Modified: | 18 Nov 2022 07:26 |
| URI: | http://eprints.triatmamulya.ac.id/id/eprint/1856 |
Actions (login required)
 |
View Item |