Real-World Gender Recognition Using Multi-order LBP and Localized Multi-Boost Learning

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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
会议日期	2015-3
会议地点	Hong Kong
关键词	Gender Recognition Multiple Order Local Binary Patterns Multi-boost Learning
英文摘要	This 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.
会议录	IEEE International Conference on Identity, Security and Behavior Analysis
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/11840]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Tieniu Tan
作者单位	模式识别国家重点实验室, 中国科学院自动化研究所
推荐引用方式 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]. 见:. Hong Kong. 2015-3.