Face detection based on Adaboost algorithm with LBP features
2015
Hochschulschrift
Zugriff:
103
Human face detection is among the most important topics in biometric research since it has a broad range of applications. Detection of face is often performed prior to recognition and tracking in biometric and surveillance systems. This paper proposes a face detection method based on Adaboost algorithm with LBP features. LBP feature is used for local feature representation due to its high discriminative power for texture classification and its invariance to global intensity variations. The best features are trained by Adaboost to form a strong classifier for distinguishing face and non-face images. In the test phrase, the input image is scaled with a number of factors to introduce a number of images of different sizes for face detection, so that faces of different sizes can be detected. Experimental results show that our method can detect faces with slight rotation, different skin colors, different facial expression, glasses and mustache.
Titel: |
Face detection based on Adaboost algorithm with LBP features
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Autor/in / Beteiligte Person: | Shih, Hao-Chen ; 石皓辰 |
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Veröffentlichung: | 2015 |
Medientyp: | Hochschulschrift |
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