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...
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2022-04-30.
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| LEADER | 02549 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_72493 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Ihza, Yusril |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Lelono, Danang |e author |
| 245 | 0 | 0 | |a Face Expression Classification in Children Using CNN |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2022-04-30. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/72493 | ||
| 520 | |a 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%. | ||
| 540 | |a Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Expression; Children; CNN; ResNet | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 2 (2022): April; 159-168 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/72493/33699 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/downloadSuppFile/72493/20197 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/72493 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/72493/33699 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/downloadSuppFile/72493/20197 |z Get Fulltext |