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Vector Boosting for Rotation Invariant Multi-View Face Detection

11 years 1 months ago
Vector Boosting for Rotation Invariant Multi-View Face Detection
In this paper, we propose a novel tree-structured multi-view 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 multi-components outputs as vectors. Our MVFD covers a large range of the face space, say, +/-45? rotation in plane (RIP) and +/-90? rotation off plane (ROP), and achieves high accuracy and amazing speed (about 40 ms per frame on a 320?240 video sequence) compared with previous published works. As a result, by simply rotating the detector 90?, 180? and 270?, a rotation invariant (360? RIP) MVFD is implemented that achieves real time performance (11 fps on a 320? 240 video sequence) with high accuracy.
Chang Huang, Haizhou Ai, Yuan Li, Shihong Lao
Added 15 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Chang Huang, Haizhou Ai, Yuan Li, Shihong Lao
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