Multilocal feature selection using genetic algorithm for face identification
Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. G...
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| Format: | Article |
| Language: | English |
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Faculty of Computer Science and Information System
2008
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| Online Access: | http://eprints.utm.my/9953/1/DzulkifliMohamad2008_MultilocalFeatureSelectionUsingGenetic.pdf |
| _version_ | 1796854611629309952 |
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| author | Mohamad, Dzulkifli |
| author_facet | Mohamad, Dzulkifli |
| author_sort | Mohamad, Dzulkifli |
| collection | ePrints |
| description | Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images. The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. Subsequently, these stages were applied to 3065 images from three distinct facial databases, namely ORL, Yale and AR. The experimental results obtained have shown that recognition rates of more than 89% have been achieved as compared to other global-based features and local facial-based feature approaches. The results also revealed that the technique is robust and invariant to translation, orientation, and scaling |
| first_indexed | 2024-03-05T18:16:39Z |
| format | Article |
| id | utm.eprints-9953 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:16:39Z |
| publishDate | 2008 |
| publisher | Faculty of Computer Science and Information System |
| record_format | dspace |
| spelling | utm.eprints-99532011-05-16T05:42:23Z http://eprints.utm.my/9953/ Multilocal feature selection using genetic algorithm for face identification Mohamad, Dzulkifli Q Science (General) QA75 Electronic computers. Computer science Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images. The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. Subsequently, these stages were applied to 3065 images from three distinct facial databases, namely ORL, Yale and AR. The experimental results obtained have shown that recognition rates of more than 89% have been achieved as compared to other global-based features and local facial-based feature approaches. The results also revealed that the technique is robust and invariant to translation, orientation, and scaling Faculty of Computer Science and Information System 2008 Article PeerReviewed application/pdf en http://eprints.utm.my/9953/1/DzulkifliMohamad2008_MultilocalFeatureSelectionUsingGenetic.pdf Mohamad, Dzulkifli (2008) Multilocal feature selection using genetic algorithm for face identification. International Journal of Image Processing, 2 (1). pp. 1-10. ISSN 1985-2304 http://www.cscjournals.org/csc/manuscript/Journals/IJIP/Volume1/Issue2/IJIP-2.pdf |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science Mohamad, Dzulkifli Multilocal feature selection using genetic algorithm for face identification |
| title | Multilocal feature selection using genetic algorithm for face identification |
| title_full | Multilocal feature selection using genetic algorithm for face identification |
| title_fullStr | Multilocal feature selection using genetic algorithm for face identification |
| title_full_unstemmed | Multilocal feature selection using genetic algorithm for face identification |
| title_short | Multilocal feature selection using genetic algorithm for face identification |
| title_sort | multilocal feature selection using genetic algorithm for face identification |
| topic | Q Science (General) QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/9953/1/DzulkifliMohamad2008_MultilocalFeatureSelectionUsingGenetic.pdf |
| work_keys_str_mv | AT mohamaddzulkifli multilocalfeatureselectionusinggeneticalgorithmforfaceidentification |