Learning Symmetry Features for Face Detection Based on Sparse Group Lasso | |
Qi Li![]() ![]() ![]() ![]() ![]() | |
2013-11 | |
会议名称 | Chinese Conference on Biometric Recognition |
会议录名称 | Chinese Conference on Biometric Recognition |
会议日期 | 2013年11月16-17日 |
会议地点 | Jinan, China |
摘要 |
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. |
关键词 | Face Detection Sparse Group Lasso Minimal Redundancy Maximal Relevance |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/11679 |
专题 | 模式识别实验室 |
通讯作者 | Li, Qi |
作者单位 | Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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chp%3A10.1007%2F978-(262KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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