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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| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 出版事項: |
IEEE Computer Society
2007
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| オンライン･アクセス: | http://eprints.utm.my/id/eprint/8769/ http://doi.ieeecomputersociety.org/10.1109/AMS.2007.38 |
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| 要約: | 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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