Face spoofing indicator using deep learning
Facial recognition is a popular biometric authentication method because of its convenience and lack of physical interaction by the end-user. However, facial recognition systems are vulnerable to face spoof attacks because of the ease to acquire people’s photos from social networking sites. Therefore...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149246 |
| _version_ | 1811687810215706624 |
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| author | Hing, Grace Minhui |
| author2 | Wen Changyun |
| author_facet | Wen Changyun Hing, Grace Minhui |
| author_sort | Hing, Grace Minhui |
| collection | NTU |
| description | Facial recognition is a popular biometric authentication method because of its convenience and lack of physical interaction by the end-user. However, facial recognition systems are vulnerable to face spoof attacks because of the ease to acquire people’s photos from social networking sites. Therefore, this project aims to tackle 2D face spoofing attacks by developing and training a deep learning model that can differentiate real and spoofed faces. The liveness detection model was trained with the collected image dataset so that it could classify and predict face detections into 2 classes, real and fake. The results showed that the model had an accuracy close to 100% that could differentiate real and spoofed faces from the video stream of the laptop’s web camera. |
| first_indexed | 2024-10-01T05:22:13Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149246 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:22:13Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1492462023-07-07T18:24:23Z Face spoofing indicator using deep learning Hing, Grace Minhui Wen Changyun School of Electrical and Electronic Engineering ECYWEN@ntu.edu.sg Engineering::Electrical and electronic engineering Facial recognition is a popular biometric authentication method because of its convenience and lack of physical interaction by the end-user. However, facial recognition systems are vulnerable to face spoof attacks because of the ease to acquire people’s photos from social networking sites. Therefore, this project aims to tackle 2D face spoofing attacks by developing and training a deep learning model that can differentiate real and spoofed faces. The liveness detection model was trained with the collected image dataset so that it could classify and predict face detections into 2 classes, real and fake. The results showed that the model had an accuracy close to 100% that could differentiate real and spoofed faces from the video stream of the laptop’s web camera. Bachelor of Engineering (Information Engineering and Media) 2021-05-29T05:46:27Z 2021-05-29T05:46:27Z 2021 Final Year Project (FYP) Hing, G. M. (2021). Face spoofing indicator using deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149246 https://hdl.handle.net/10356/149246 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Hing, Grace Minhui Face spoofing indicator using deep learning |
| title | Face spoofing indicator using deep learning |
| title_full | Face spoofing indicator using deep learning |
| title_fullStr | Face spoofing indicator using deep learning |
| title_full_unstemmed | Face spoofing indicator using deep learning |
| title_short | Face spoofing indicator using deep learning |
| title_sort | face spoofing indicator using deep learning |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/149246 |
| work_keys_str_mv | AT hinggraceminhui facespoofingindicatorusingdeeplearning |