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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yen-Yu Lin Tyng-Luh Liu |
| Copyright Year | 2005 |
| Description | Author affiliation: Inst. of Inf. Sci., Acad. Sinica, Taipei, Taiwan (Yen-Yu Lin; Tyng-Luh Liu) |
| Abstract | 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. |
| Sponsorship | IEEE Comput. Soc |
| Starting Page | 680 |
| Ending Page | 687 |
| File Size | 1075449 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769523722 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2005.307 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Face detection Boosting Detectors Neural networks Information science Multi-layer neural network Multilayer perceptrons Testing Computer vision |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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