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Robust Face Detection with Multi-Class Boosting

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Robust Face Detection with Multi-Class Boosting
With the aim to design a general learning framework for detecting faces of various poses or under different lighting conditions, we are motivated to formulate the task as a classification problem over data of multiple classes. Specifically, our approach focuses on a new multi-class boosting algorithm, called MBHboost, and its integration with a cascade structure for effectively performing face detection. There are three main advantages of using MBHboost: 1) each MBH weak learner is derived by sharing a good projection direction such that each class of data has its own decision boundary; 2) the proposed boosting algorithm is established based on an optimal criterion for multi-class classification; and 3) since MBHboost is flexible with respect to the number of classes, it turns out that it is possible to use only one single boosted cascade for the multi-class detection. All these properties give rise to a robust system to detect faces efficiently and accurately.
Yen-Yu Lin, Tyng-Luh Liu
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CVPR
Authors Yen-Yu Lin, Tyng-Luh Liu
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