Maximisation of mutual information for gait-based soft biometric classification using Gabor features
文献类型：期刊论文
| 作者 | Maodi Hu; Yunhong Wang; Zhaoxiang Zhang
|
| 刊名 | IET Biometrics
 |
| 出版日期 | 2012-04-12 |
| 卷号 | 1期号:1页码:55-62 |
| 关键词 | Feature Extraction Gabor Filters Gait Analysis Gender Issues Hidden Markov Models Image Enhancement |
| 英文摘要 | Besides identity, soft biometric characteristics, such as gender and age can also be derived from gait patterns. With Gabor enhancement, supervised learning and temporal modelling, the authors present a robust framework to achieve state-of-the-art classification accuracy for both gender and age. Gabor filter and maximisation of mutual information are used to extract low-dimensional features, whereas Bayes rules based on hidden Markov models (HMMs) are adopted for soft biometric classification. The multi-view soft biometric classification problem is defined as two different cases, saying, one-to-one view and many-to-one view, according to the number of available gallery views. In case more than one gallery view is available, the multi-view soft biometric classification problem is hierarchically solved with a view-related population HMM, in which the estimated view angle is treated as the intermediate result in the first stage. Performance has been evaluated on benchmark databases, which verify the advantages of the proposed algorithm. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13236]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Maodi Hu |
| 推荐引用方式 GB/T 7714 | Maodi Hu,Yunhong Wang,Zhaoxiang Zhang. Maximisation of mutual information for gait-based soft biometric classification using Gabor features[J]. IET Biometrics,2012,1(1):55-62. |
| APA | Maodi Hu,Yunhong Wang,&Zhaoxiang Zhang.(2012).Maximisation of mutual information for gait-based soft biometric classification using Gabor features.IET Biometrics,1(1),55-62. |
| MLA | Maodi Hu,et al."Maximisation of mutual information for gait-based soft biometric classification using Gabor features".IET Biometrics 1.1(2012):55-62. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。