Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value
This paper present a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system fo...
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| Format: | Article |
| Language: | English |
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Serials Publications
2011
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| Online Access: | http://eprints.utem.edu.my/id/eprint/4427/ http://eprints.utem.edu.my/id/eprint/4427/1/IJCSES.PDF |
| _version_ | 1848887030524674048 |
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| author | A Aziz, Khairul Azha Abdullah, Shahrum Shah Mohd Jahari @ Mohd Johari, Ahmad Nizam |
| author_facet | A Aziz, Khairul Azha Abdullah, Shahrum Shah Mohd Jahari @ Mohd Johari, Ahmad Nizam |
| author_sort | A Aziz, Khairul Azha |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | This paper present a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteria. In this research, the best setting for RBF face detection were summarized into one table where by using center 200 and spread 4 gives the highest detection rate and the lowest FAR as well as FRR. But for detecting many faces in a single image, center 200 and spread 5 is the best setting as the system can detect all faces in the image. |
| first_indexed | 2025-11-15T19:47:54Z |
| format | Article |
| id | utem-4427 |
| institution | Universiti Teknikal Malaysia Melaka |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:47:54Z |
| publishDate | 2011 |
| publisher | Serials Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utem-44272022-01-05T14:47:05Z http://eprints.utem.edu.my/id/eprint/4427/ Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value A Aziz, Khairul Azha Abdullah, Shahrum Shah Mohd Jahari @ Mohd Johari, Ahmad Nizam TK Electrical engineering. Electronics Nuclear engineering This paper present a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteria. In this research, the best setting for RBF face detection were summarized into one table where by using center 200 and spread 4 gives the highest detection rate and the lowest FAR as well as FRR. But for detecting many faces in a single image, center 200 and spread 5 is the best setting as the system can detect all faces in the image. Serials Publications 2011-07 Article NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/4427/1/IJCSES.PDF A Aziz, Khairul Azha and Abdullah, Shahrum Shah and Mohd Jahari @ Mohd Johari, Ahmad Nizam (2011) Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value. International Journal of Computer Sciences and Engineering Systems, 5 (3). pp. 145-151. ISSN 0973-4406 http://bit.kuas.edu.tw/~ijcses/ |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering A Aziz, Khairul Azha Abdullah, Shahrum Shah Mohd Jahari @ Mohd Johari, Ahmad Nizam Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value |
| title | Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value |
| title_full | Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value |
| title_fullStr | Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value |
| title_full_unstemmed | Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value |
| title_short | Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value |
| title_sort | face detection using radial basis function neural networks with fixed spread value |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utem.edu.my/id/eprint/4427/ http://eprints.utem.edu.my/id/eprint/4427/ http://eprints.utem.edu.my/id/eprint/4427/1/IJCSES.PDF |