In this paper, we propose a novel tree-structured multiview face detector (MVFD), which adopts the coarse-to-fine strategy to divide the entire face space into smaller and smaller subspaces. For this purpose, a newly extended boosting algorithm named vector boosting is developed to train the predictors for the branching nodes of the tree that have multicomponents outputs as vectors. Our MVFD covers a large range of the face space, say, +/-45/spl deg/ rotation in plane (RIP) and +/-90/spl deg/ rotation off plane (ROP), and achieves high accuracy and amazing speed (about 40 ms per frame on a 320 /spl times/ 240 video sequence) compared with previous published works. As a result, by simply rotating the detector 90/spl deg/, 180/spl deg/ and 270/spl deg/, a rotation invariant (360/spl deg/ RIP) MVFD is implemented that achieves real time performance (11 fps on a 320 /spl times/ 240 video sequence) with high accuracy.
Vector boosting for rotation invariant multi-view face detection
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 ; 1 ; 446-453 Vol. 1
2005-01-01
1050080 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vector Boosting for Rotation Invariant Multi-View Face Detection
British Library Conference Proceedings | 2005
|Robust face detection with multi-class boosting
IEEE | 2005
|Support vector machine based multi-view face detection and recognition
British Library Online Contents | 2004
|View-Invariant Face Detection Method Based on Local PCA Cells
British Library Conference Proceedings | 2003
|