CASIA OpenIR  > 智能感知与计算研究中心
Learning Symmetry Features for Face Detection Based on Sparse Group Lasso
Qi Li; Zhenan Sun; Ran He(赫然); Tieniu Tan; Li, Qi
2013-11
Conference NameChinese Conference on Biometric Recognition
Source PublicationChinese Conference on Biometric Recognition
Conference Date2013年11月16-17日
Conference PlaceJinan, China
Abstract
Face detection is of fundamental importance in face recognition, facial expression recognition and other face biometrics related applications. The core problem of face detection is to select a subset of features from massive local appearance descriptors such as Haar features and LBP. This paper proposes a two stage feature selection method for face detection. Firstly, feature representation of the symmetric characteristics of face pattern is formulated as a structured sparsity problem and sparse group lasso is used to select the most effective local features for face detection. Secondly, minimal redundancy maximal relevance is used to remove the redundant features in group sparsity learning. Experimental results demonstrate that the proposed feature selection method has better generalization ability than Adaboost and Lasso based feature selection methods for face detection problems.
KeywordFace Detection Sparse Group Lasso Minimal Redundancy Maximal Relevance
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11679
Collection智能感知与计算研究中心
Corresponding AuthorLi, Qi
AffiliationCenter for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Qi Li,Zhenan Sun,Ran He,et al. Learning Symmetry Features for Face Detection Based on Sparse Group Lasso[C],2013.
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