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2006
IEEE

Learning Sparse Features in Granular Space for Multi-View Face Detection

9 years 5 months ago
Learning Sparse Features in Granular Space for Multi-View Face Detection
In this paper, a novel sparse feature set is introduced into the Adaboost learning framework for multi-view face detection (MVFD), and a learning algorithm based on heuristic search is developed to select sparse features in granular space. Compared with Haar-like features, sparse features are more generic and powerful to characterize multi-view face pattern that is more diverse and asymmetric than frontal face pattern. In order to cut down search space to a manageable size, we propose a multi-scaled search algorithm that is about 6 times faster than brute-force search. With this method, a MVFD system is implemented that covers face pose changes over +/-45° rotation in plane (RIP) and +/-90° rotation off plane (ROP). Experiments over well-know test set are reported to show its high performance in both accuracy and speed.
Chang Huang, Haizhou Ai, Yuan Li, Shihong Lao
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where FGR
Authors Chang Huang, Haizhou Ai, Yuan Li, Shihong Lao
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