Facial Expression Recognition using Residual Convnet with Image Augmentations

Facial Expression Recognition using Residual Convnet with Image Augmentations

facial expression recognition, CNN, ResNet, Mish, Accuracy Booster Plus

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 neura 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 https://github.com/yusufrahadika/facial-expressions-essay

Fadhil Yusuf Rahadika, Novanto Yudistira, Yuita Arum Sari

http://dx.doi.org/10.21609/jiki.v14i2.968

Faculty of Computer Science Universitas Indonesia

2021-07-04

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)