Blurred face recognition using CNN
Facial recognition systems play a crucial role in numerous applications ranging from security to healthcare purposes. Regardless, these systems face challenges when the images provided are from video sources or of low-quality. In this project, we explore ResNet18, a type of CNN model on KDEF dataset...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177047 |
| _version_ | 1811690193971838976 |
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| author | Kabyar, Myet Wun |
| author2 | Anamitra Makur |
| author_facet | Anamitra Makur Kabyar, Myet Wun |
| author_sort | Kabyar, Myet Wun |
| collection | NTU |
| description | Facial recognition systems play a crucial role in numerous applications ranging from security to healthcare purposes. Regardless, these systems face challenges when the images provided are from video sources or of low-quality. In this project, we explore ResNet18, a type of CNN model on KDEF dataset to address the issue of blurred face recognition, focusing on a blur filter with Gaussian Blur.
The objectives of this project was to implement a machine learning model which was able to accurately recognise blurred facial expressions and to evaluate this model using real-world datasets. To achieve these objectives, the KDEF Dataset was used as it had the seven basic facial expressions taken from different angles. Additionally, Gaussian Blur with varying kernel sizes were used to simulate real-world examples whereby the images may be distorted or blurred.
The key findings of this project shows the impact of blur filters on the accuracy of facial recognition systems. While Gaussian blur was observed to enhance certain facial features, it may also introduce errors in classification. In general, the accuracy of the model was evaluated to decrease as the Gaussian blur with a kernel size continued to increase. In conclusion, this project contributed to the understanding of blurred facial expression recognition and the importance of robust CNN models for real-life scenarios. Future research such as exploring different types of blur filters and increasing the iterations of the evaluation phases were recommended to cater for different types of facial expressions when blurred. |
| first_indexed | 2024-10-01T06:00:07Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177047 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:00:07Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1770472024-05-24T15:44:37Z Blurred face recognition using CNN Kabyar, Myet Wun Anamitra Makur School of Electrical and Electronic Engineering EAMakur@ntu.edu.sg Engineering Convolutional neural network Facial recognition systems play a crucial role in numerous applications ranging from security to healthcare purposes. Regardless, these systems face challenges when the images provided are from video sources or of low-quality. In this project, we explore ResNet18, a type of CNN model on KDEF dataset to address the issue of blurred face recognition, focusing on a blur filter with Gaussian Blur. The objectives of this project was to implement a machine learning model which was able to accurately recognise blurred facial expressions and to evaluate this model using real-world datasets. To achieve these objectives, the KDEF Dataset was used as it had the seven basic facial expressions taken from different angles. Additionally, Gaussian Blur with varying kernel sizes were used to simulate real-world examples whereby the images may be distorted or blurred. The key findings of this project shows the impact of blur filters on the accuracy of facial recognition systems. While Gaussian blur was observed to enhance certain facial features, it may also introduce errors in classification. In general, the accuracy of the model was evaluated to decrease as the Gaussian blur with a kernel size continued to increase. In conclusion, this project contributed to the understanding of blurred facial expression recognition and the importance of robust CNN models for real-life scenarios. Future research such as exploring different types of blur filters and increasing the iterations of the evaluation phases were recommended to cater for different types of facial expressions when blurred. Bachelor's degree 2024-05-24T11:47:34Z 2024-05-24T11:47:34Z 2024 Final Year Project (FYP) Kabyar, M. W. (2024). Blurred face recognition using CNN. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177047 https://hdl.handle.net/10356/177047 en A3006-231 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Convolutional neural network Kabyar, Myet Wun Blurred face recognition using CNN |
| title | Blurred face recognition using CNN |
| title_full | Blurred face recognition using CNN |
| title_fullStr | Blurred face recognition using CNN |
| title_full_unstemmed | Blurred face recognition using CNN |
| title_short | Blurred face recognition using CNN |
| title_sort | blurred face recognition using cnn |
| topic | Engineering Convolutional neural network |
| url | https://hdl.handle.net/10356/177047 |
| work_keys_str_mv | AT kabyarmyetwun blurredfacerecognitionusingcnn |