The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions
The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer...
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38655/ http://umpir.ump.edu.my/id/eprint/38655/1/The%20Implementation%20of%20Transfer%20Learning%20by%20Convolution%20Neural%20Network%20%28CNN%29%20for%20Recognizing%20Facial%20Emotions.pdf |
| _version_ | 1848825563742994432 |
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| author | Munanday, Anbananthan Pillai Norazlianie, Sazali Asogan, Arjun Ramasamy, Devarajan Ahmad Shahir, Jamaludin |
| author_facet | Munanday, Anbananthan Pillai Norazlianie, Sazali Asogan, Arjun Ramasamy, Devarajan Ahmad Shahir, Jamaludin |
| author_sort | Munanday, Anbananthan Pillai |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniquesthat incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy. |
| first_indexed | 2025-11-15T03:30:55Z |
| format | Article |
| id | ump-38655 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:30:55Z |
| publishDate | 2023 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-386552023-09-20T01:06:41Z http://umpir.ump.edu.my/id/eprint/38655/ The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions Munanday, Anbananthan Pillai Norazlianie, Sazali Asogan, Arjun Ramasamy, Devarajan Ahmad Shahir, Jamaludin QA76 Computer software TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TS Manufactures The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniquesthat incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy. Semarak Ilmu Publishing 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38655/1/The%20Implementation%20of%20Transfer%20Learning%20by%20Convolution%20Neural%20Network%20%28CNN%29%20for%20Recognizing%20Facial%20Emotions.pdf Munanday, Anbananthan Pillai and Norazlianie, Sazali and Asogan, Arjun and Ramasamy, Devarajan and Ahmad Shahir, Jamaludin (2023) The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions. Journal of Advanced Research in Applied Sciences and Engineering Technology, 32 (2). pp. 255-276. ISSN 2462-1943. (Published) https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/2434 |
| spellingShingle | QA76 Computer software TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TS Manufactures Munanday, Anbananthan Pillai Norazlianie, Sazali Asogan, Arjun Ramasamy, Devarajan Ahmad Shahir, Jamaludin The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions |
| title | The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions |
| title_full | The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions |
| title_fullStr | The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions |
| title_full_unstemmed | The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions |
| title_short | The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions |
| title_sort | implementation of transfer learning by convolution neural network (cnn) for recognizing facial emotions |
| topic | QA76 Computer software TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/38655/ http://umpir.ump.edu.my/id/eprint/38655/ http://umpir.ump.edu.my/id/eprint/38655/1/The%20Implementation%20of%20Transfer%20Learning%20by%20Convolution%20Neural%20Network%20%28CNN%29%20for%20Recognizing%20Facial%20Emotions.pdf |