Face detection using artificial neural network approach
A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the adaboost learning algorithm to select a small number of critical visual features from...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
IEEE Computer Society
2007
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| Online Access: | http://eprints.utm.my/8769/ http://doi.ieeecomputersociety.org/10.1109/AMS.2007.38 |
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| _version_ | 1845472075278450688 |
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| author | Khalid, Marzuki Jumari, Khairol Faisal Nazeer, Shahrin Azuan Omar, Nazaruddin |
| author_facet | Khalid, Marzuki Jumari, Khairol Faisal Nazeer, Shahrin Azuan Omar, Nazaruddin |
| author_sort | Khalid, Marzuki |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the adaboost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance.
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| format | Article |
| id | my.utm.eprints-8769 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2007 |
| publisher | IEEE Computer Society |
| record_format | eprints |
| spelling | my.utm.eprints-87692017-10-19T03:44:30Z http://eprints.utm.my/8769/ Face detection using artificial neural network approach Khalid, Marzuki Jumari, Khairol Faisal Nazeer, Shahrin Azuan Omar, Nazaruddin Q Science (General) A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the adaboost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance. IEEE Computer Society 2007 Article PeerReviewed Khalid, Marzuki and Jumari, Khairol Faisal and Nazeer, Shahrin Azuan and Omar, Nazaruddin (2007) Face detection using artificial neural network approach. First Asia International Conference On Modeling & Simulation (AMS 2007) . pp. 394-399. http://doi.ieeecomputersociety.org/10.1109/AMS.2007.38 10.1109/AMS.2007.38 |
| spellingShingle | Q Science (General) Khalid, Marzuki Jumari, Khairol Faisal Nazeer, Shahrin Azuan Omar, Nazaruddin Face detection using artificial neural network approach |
| title | Face detection using artificial neural network approach
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| title_full | Face detection using artificial neural network approach
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| title_fullStr | Face detection using artificial neural network approach
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| title_full_unstemmed | Face detection using artificial neural network approach
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| title_short | Face detection using artificial neural network approach
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| title_sort | face detection using artificial neural network approach |
| topic | Q Science (General) |
| url | http://eprints.utm.my/8769/ http://doi.ieeecomputersociety.org/10.1109/AMS.2007.38 |
| url_provider | http://eprints.utm.my/ |