Real-World Gender Recognition Using Multi-order LBP and Localized Multi-Boost Learning
文献类型：会议论文
| 作者 | Cao Dong(曹冬) ; Ran He(赫然); Man Zhang; Zhenan Sun; Tieniu Tan
|
| 出版日期 | 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
 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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