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Learning Sparse Features in Granular Space for Multi-View Face Detection Chang HUANG1
 

Summary: Learning Sparse Features in Granular Space for Multi-View Face Detection
Chang HUANG1
, Haizhou AI1
, Yuan LI1
and Shihong LAO2
1
Computer Science and Technology Department, Tsinghua University, Beijing 100084, China
2
Sensing and Control Technology Laboratory, Omron Corporation, Kyoto 619-0283, Japan
E-mail: ahz@mail.tsinghua.edu.cn
Abstract
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

  

Source: Ai, Haizhou - Department of Computer Science and Technology, Tsinghua University

 

Collections: Computer Technologies and Information Sciences