Agung, Erlangga Satrio and Rifai, Achmad Pratama and Wijayanto, Titis (2024) Image-based facial emotion recognition using convolutional neural network on emognition dataset. Scientific Reports, 14 (1): 14429. ISSN 20452322
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Abstract
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96 and 0.95, respectively, on the test data. © The Author(s) 2024.
Item Type: | Article |
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Additional Information: | Cited by: 5; All Open Access, Gold Open Access |
Uncontrolled Keywords: | Facial emotion recognition, Convolutional neural network, Deep learning, Emognition dataset |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Faculty of Engineering > Mechanical and Industrial Engineering Department |
Depositing User: | Yulistiarini Kumaraningrum KUMARANINGRUM |
Date Deposited: | 05 Nov 2024 03:13 |
Last Modified: | 05 Nov 2024 03:13 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10705 |