Techniques for face recognition in surveillance applications
This is a final year report aiming to provide the knowledge and achievements acquired upon completion of the final year project. It covers the motivations for the project and project objectives, scope and background knowledge required carrying out the project. In this project, the main focus is t...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10356/55220 |
| _version_ | 1811697746013323264 |
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| author | Htun, Naw Olive. |
| author2 | Yap Kim Hui |
| author_facet | Yap Kim Hui Htun, Naw Olive. |
| author_sort | Htun, Naw Olive. |
| collection | NTU |
| description | This is a final year report aiming to provide the knowledge and achievements acquired upon completion of the final year project. It covers the motivations for the project and project objectives, scope and background knowledge required carrying out the project.
In this project, the main focus is to study still-image face recognition system and thus theoretical backgrounds for the two essential algorithms in recognizing the different faces and the neural network used to implement the face recognition are briefly presented in this report. The main algorithms used in this project are the two appearance-based face recognition algorithms: Eigenface and Fisherface. The face databases used with the programs are also briefly described. The backpropagation neural network is utilized as a classifier and the face images from the databases are presented into the network to perform face recognition. Also a user friendly graphical user interface (GUI) acting as a platform for face recognition is also introduced.
Finally, the correct classification rates based on the conditions that images were taken are then analyzed. After the evaluating the results, it is observed that Fisherface method has better performance under varying lighting condition. It is also observed that the performance of Eigenface improved after removing the first three components of the Eigenfaces. |
| first_indexed | 2024-10-01T08:00:09Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/55220 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T08:00:09Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/552202023-07-07T17:13:04Z Techniques for face recognition in surveillance applications Htun, Naw Olive. Yap Kim Hui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems This is a final year report aiming to provide the knowledge and achievements acquired upon completion of the final year project. It covers the motivations for the project and project objectives, scope and background knowledge required carrying out the project. In this project, the main focus is to study still-image face recognition system and thus theoretical backgrounds for the two essential algorithms in recognizing the different faces and the neural network used to implement the face recognition are briefly presented in this report. The main algorithms used in this project are the two appearance-based face recognition algorithms: Eigenface and Fisherface. The face databases used with the programs are also briefly described. The backpropagation neural network is utilized as a classifier and the face images from the databases are presented into the network to perform face recognition. Also a user friendly graphical user interface (GUI) acting as a platform for face recognition is also introduced. Finally, the correct classification rates based on the conditions that images were taken are then analyzed. After the evaluating the results, it is observed that Fisherface method has better performance under varying lighting condition. It is also observed that the performance of Eigenface improved after removing the first three components of the Eigenfaces. Bachelor of Engineering 2013-12-30T07:10:25Z 2013-12-30T07:10:25Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/55220 en Nanyang Technological University 52 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Htun, Naw Olive. Techniques for face recognition in surveillance applications |
| title | Techniques for face recognition in surveillance applications |
| title_full | Techniques for face recognition in surveillance applications |
| title_fullStr | Techniques for face recognition in surveillance applications |
| title_full_unstemmed | Techniques for face recognition in surveillance applications |
| title_short | Techniques for face recognition in surveillance applications |
| title_sort | techniques for face recognition in surveillance applications |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/55220 |
| work_keys_str_mv | AT htunnawolive techniquesforfacerecognitioninsurveillanceapplications |