Face Expression Classification in Children Using CNN
One of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emoti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2022-04-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/72493 |
| _version_ | 1811238369328365568 |
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| author | Yusril Ihza Danang Lelono |
| author_facet | Yusril Ihza Danang Lelono |
| author_sort | Yusril Ihza |
| collection | DOAJ |
| description | One of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emotions, for example, when children are angry, sometimes they show an expressionless face, making it difficult to know what emotions the child is experiencing. Therefore, it is proposed research using Convolutional Neural Network with ResNet-50 architecture. According to [1] CNN Resnet-50 is superior to other facial recognition methods, specifically in the classification of facial expressions. CNN ResNet-50 generates a model during the training process, and the model will be used during the testing process. The dataset used is Children's Spontaneous facial Expressions (LIRIS-CSE) data proposed by [2]. CNN ResNet-50 can identify children's expressions well, including expressions of anger, disgust, fear, happy, sad and surprise. The results showed a very significant increase in accuracy, namely in testing data testing reached 99.89%. |
| first_indexed | 2024-04-12T12:41:41Z |
| format | Article |
| id | doaj.art-8180f682e4844bc4ac65a551ad5e3d89 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-04-12T12:41:41Z |
| publishDate | 2022-04-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-8180f682e4844bc4ac65a551ad5e3d892022-12-22T03:32:46ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582022-04-0116215916810.22146/ijccs.7249331715Face Expression Classification in Children Using CNNYusril Ihza0Danang Lelono1Master Program of Computer Science, FMIPA UGM, YogyakartaDepartment of Computer Science and Electronics, FMIPA UGM, YogyakartaOne of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emotions, for example, when children are angry, sometimes they show an expressionless face, making it difficult to know what emotions the child is experiencing. Therefore, it is proposed research using Convolutional Neural Network with ResNet-50 architecture. According to [1] CNN Resnet-50 is superior to other facial recognition methods, specifically in the classification of facial expressions. CNN ResNet-50 generates a model during the training process, and the model will be used during the testing process. The dataset used is Children's Spontaneous facial Expressions (LIRIS-CSE) data proposed by [2]. CNN ResNet-50 can identify children's expressions well, including expressions of anger, disgust, fear, happy, sad and surprise. The results showed a very significant increase in accuracy, namely in testing data testing reached 99.89%.https://jurnal.ugm.ac.id/ijccs/article/view/72493expressionchildrencnnresnet |
| spellingShingle | Yusril Ihza Danang Lelono Face Expression Classification in Children Using CNN IJCCS (Indonesian Journal of Computing and Cybernetics Systems) expression children cnn resnet |
| title | Face Expression Classification in Children Using CNN |
| title_full | Face Expression Classification in Children Using CNN |
| title_fullStr | Face Expression Classification in Children Using CNN |
| title_full_unstemmed | Face Expression Classification in Children Using CNN |
| title_short | Face Expression Classification in Children Using CNN |
| title_sort | face expression classification in children using cnn |
| topic | expression children cnn resnet |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/72493 |
| work_keys_str_mv | AT yusrilihza faceexpressionclassificationinchildrenusingcnn AT dananglelono faceexpressionclassificationinchildrenusingcnn |