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Real-World Gender Recognition Using Multi-order LBP and Localized Multi-Boost Learning
Cao Dong(曹冬); Ran He(赫然); Man Zhang; Zhenan Sun; Tieniu Tan
2015
Conference NameIEEE International Conference on Identity, Security and Behavior Analysis
Source PublicationIEEE International Conference on Identity, Security and Behavior Analysis
Conference Date2015-3
Conference PlaceHong Kong
AbstractThis paper presents a new approach for real-world gender recognition, where images are captured under uncontrolled environments with various poses, illuminations and expressions. While a large number of gender recognition methods have been introduced in recent years, most of them describe each image in a single feature space or simple combination of multiple individual spaces, which can not be powerful enough to alleviate the noise in real-world scenarios. To address this, we propose exploring multiple order local binary patterns (MOLBP) as features for learning, and develop a localized multi-boost learning (LMBL) algorithm to combine the different features for classification. Experimental results show that the proposed algorithm outperforms state-of-the-art methods in two real-world. datasets.
KeywordGender Recognition Multiple Order Local Binary Patterns Multi-boost Learning
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11840
Collection智能感知与计算研究中心
Corresponding AuthorTieniu Tan
Affiliation模式识别国家重点实验室, 中国科学院自动化研究所
Recommended Citation
GB/T 7714
Cao Dong,Ran He,Man Zhang,et al. Real-World Gender Recognition Using Multi-order LBP and Localized Multi-Boost Learning[C],2015.
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