Feature level fusion method based on the coupled metric learning and its application in gait recognition

被引：0

作者：

Wang, Kejun ^{[1
]}

Yan, Tao ^{[1
]}

Lü, Zhuowen ^{[1
]}

机构：

[1] College of Automation, Harbin Engineering University

来源：

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2013年 / 43卷 / SUPPL.I期

关键词：

Coupled metric learning; Feature level fusion; Gait energy image; Gait recognition;

D O I：

10.3969/j.issn.1001-0505.2013.S1.002

中图分类号：

学科分类号：

摘要：

The coupled metric learning method is applied to the data fusion field since it can directly deal with different datasets. A feature level fusion method based on the coupled metric learning is proposed. First, by adding the optimization of the correlation data in original single set, the objective function of the coupled metric learning method improves as all the projection features with correlation in the coupled space are close to each other. The overall distribution of these features becomes more suitable for feature level fusion. Then, the features are fused in serial mode. Finally, more effective features are obtained for classification. The proposed method is applied to solve the data fusion problem in gait recognition. The experiments and analysis are made based on the CASIA(B) gait database. The experimental results show that the proposed method can achieve good recognition results.

引用

页码：7 / 11

页数：4

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