Anbananthan Pillai, Munanday (2022) Facial recognition for human disposition identification. College of Engineering, Universiti Malaysia Pahang.
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EA18181_ANBANANTHAN_THESIS - Anba Munanday.pdf - Accepted Version Download (4MB) | Preview |
Abstract
Human disposition identification and recognition has become one of the popular topics under OpenCV based on deep learning. The importance of this project is to recognize facial expressions. Here, the discussion will be done about the deep learning models and use it properly that can assist the image processing. There are many deep learning models and the suitable model for this project chose according to the ability to meet the system operation requirements such as speed and accuracy. Evolutionary methodology was implemented in this system design by using several image processing techniques include image acquisition, image enhancement (or known as pre-processing stages) and feature extraction. The system first applies some pre-processing stages to enhance the input image and reduce the noise. The face boundary will then be detected. The region of interest such as mouth and eyes will be determined, from which, features will be extracted. Finally, the face will be classified into classes using the CNN model based on the features extracted. The method was applied and tested on a dataset of faces (FER-2013) and the success rate obtained was 92.86%. For this project, it is targeted to get the accurate detection of human dispositions through the application and extract the emotions/classes in percentage.
| Item Type: | Undergraduates Project Papers |
|---|---|
| Additional Information: | SV: Assoc. Prof. Ir. Ts. Dr. Fahmi Bin Samsuri |
| Uncontrolled Keywords: | Facial recognition |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | College of Engineering |
| Depositing User: | Mr. Nik Ahmad Nasyrun Nik Abd Malik |
| Date Deposited: | 25 Oct 2023 07:33 |
| Last Modified: | 25 Oct 2023 07:33 |
| URI: | http://umpir.ump.edu.my/id/eprint/39022 |
| Download Statistic: | View Download Statistics |
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