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High-Performance Rotation Invariant Multiview Face Detection
April 2007 (vol. 29 no. 4)
pp. 671-686
Rotation invariant multiview face detection (MVFD) aims to detect faces with arbitrary rotation-in-plane (RIP) and rotation-off-plane (ROP) angles in still images or video sequences. MVFD is crucial as the first step in automatic face processing for general applications since face images are seldom upright and frontal unless they are taken cooperatively. In this paper, we propose a series of innovative methods to construct a high-performance rotation invariant multiview face detector, including the Width-First-Search (WFS) tree detector structure, the Vector Boosting algorithm for learning vector-output strong classifiers, the domain-partition-based weak learning method, the sparse feature in granular space, and the heuristic search for sparse feature selection. As a result of that, our multiview face detector achieves low computational complexity, broad detection scope, and high detection accuracy on both standard testing sets and real-life images.
Index Terms:
Pattern classification, AdaBoost, vector boosting, granular feature, rotation invariant, face detection.
Citation:
Chang Huang, Haizhou Ai, Yuan Li, Shihong Lao, "High-Performance Rotation Invariant Multiview Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 671-686, April 2007, doi:10.1109/TPAMI.2007.1011
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