Facial micro-expression analysis
Individuals' emotional states are revealed through their facial expressions. Nevertheless, emotions can be concealed and portrayed differently, making discerning an individual's genuine emotion challenging. Additionally, in medical settings, individuals may be reluctant to discuss their ge...
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| Tác giả khác: | |
| Định dạng: | Final Year Project (FYP) |
| Ngôn ngữ: | English |
| Được phát hành: |
Nanyang Technological University
2023
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://hdl.handle.net/10356/165884 |
| _version_ | 1826111671812227072 |
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| author | Ong, Si Ying |
| author2 | Zheng Jianmin |
| author_facet | Zheng Jianmin Ong, Si Ying |
| author_sort | Ong, Si Ying |
| collection | NTU |
| description | Individuals' emotional states are revealed through their facial expressions. Nevertheless, emotions can be concealed and portrayed differently, making discerning an individual's genuine emotion challenging. Additionally, in medical settings, individuals may be reluctant to discuss their genuine emotions and condition during a remote consultation, which reduces the accuracy of the medical diagnosis. Patients may be reluctant to address particular problems or hesitant to seek help. As a result, the utilization of facial micro-expression analysis technologies enables the decoding of deliberately disguised subtle emotions. Therefore, the research intends to employ such technology to enable healthcare professionals to interpret a patient's genuine emotions and administer appropriate medical care or consolation, if necessary, facilitating accurate medical diagnosis and proper treatment.
The report proposed developing a real-time mobile application that incorporates neural network models into tele-consultation settings in an Android mobile application that accurately recognizes facial micro-expressions. To achieve a reputable performance for the system, the model employs Advanced Attention Network (AAN) inspired by neural network models Distract Your Attention Network (DAN) and Convolutional Block Attention Module (CBAM), as well as a series of experiments with different configurations and hyperparameters, was conducted. Based on the results, the AAN model performance was inadequate and the research implemented more trials with various configurations and model fine-tuning. Further experiment implies that the positioning of the attention mechanism, CBAM, and the hyperparameters and architecture of neural network models influence their performance, resulting in improved accuracy.
The research implications imply that more experiments can be conducted to examine the effects of adjusting the position of CBAM and other hyperparameters, which may result in greater accuracy in identifying "genuine" emotion during tele-consultation. |
| first_indexed | 2024-10-01T02:54:38Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/165884 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:54:38Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1658842023-04-21T15:39:15Z Facial micro-expression analysis Ong, Si Ying Zheng Jianmin School of Computer Science and Engineering ASJMZheng@ntu.edu.sg Engineering::Computer science and engineering Individuals' emotional states are revealed through their facial expressions. Nevertheless, emotions can be concealed and portrayed differently, making discerning an individual's genuine emotion challenging. Additionally, in medical settings, individuals may be reluctant to discuss their genuine emotions and condition during a remote consultation, which reduces the accuracy of the medical diagnosis. Patients may be reluctant to address particular problems or hesitant to seek help. As a result, the utilization of facial micro-expression analysis technologies enables the decoding of deliberately disguised subtle emotions. Therefore, the research intends to employ such technology to enable healthcare professionals to interpret a patient's genuine emotions and administer appropriate medical care or consolation, if necessary, facilitating accurate medical diagnosis and proper treatment. The report proposed developing a real-time mobile application that incorporates neural network models into tele-consultation settings in an Android mobile application that accurately recognizes facial micro-expressions. To achieve a reputable performance for the system, the model employs Advanced Attention Network (AAN) inspired by neural network models Distract Your Attention Network (DAN) and Convolutional Block Attention Module (CBAM), as well as a series of experiments with different configurations and hyperparameters, was conducted. Based on the results, the AAN model performance was inadequate and the research implemented more trials with various configurations and model fine-tuning. Further experiment implies that the positioning of the attention mechanism, CBAM, and the hyperparameters and architecture of neural network models influence their performance, resulting in improved accuracy. The research implications imply that more experiments can be conducted to examine the effects of adjusting the position of CBAM and other hyperparameters, which may result in greater accuracy in identifying "genuine" emotion during tele-consultation. Bachelor of Engineering (Computer Science) 2023-04-16T08:49:24Z 2023-04-16T08:49:24Z 2023 Final Year Project (FYP) Ong, S. Y. (2023). Facial micro-expression analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165884 https://hdl.handle.net/10356/165884 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Ong, Si Ying Facial micro-expression analysis |
| title | Facial micro-expression analysis |
| title_full | Facial micro-expression analysis |
| title_fullStr | Facial micro-expression analysis |
| title_full_unstemmed | Facial micro-expression analysis |
| title_short | Facial micro-expression analysis |
| title_sort | facial micro expression analysis |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/165884 |
| work_keys_str_mv | AT ongsiying facialmicroexpressionanalysis |