Illumination Invariant Face Detection Using Classifier Fusion
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Object detection using spatial histogram features
Image and Vision Computing
Fast learning for statistical face detection
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Rotated haar-like features for face detection with in-plane rotation
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
Learning to detect objects of many classes using binary classifiers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Role-based identity recognition for TV broadcasts
Multimedia Tools and Applications
| Hi-index | 0.00 |
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.