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 | |
会议名称 | IEEE International Conference on Identity, Security and Behavior Analysis |
会议录名称 | IEEE International Conference on Identity, Security and Behavior Analysis |
会议日期 | 2015-3 |
会议地点 | Hong Kong |
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. |
关键词 | Gender Recognition Multiple Order Local Binary Patterns Multi-boost Learning |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/11840 |
专题 | 智能感知与计算 |
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 |
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 | 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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Real-World Gender Re(1073KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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