Human Gait Recognition using Dual-Tree Complex Wavelet Transform

被引：0
|
作者：
Badiezadeh, Ali [1 ]
Ayatollahi, Fazael [1 ]
Ghaeminia, Mohammad H. [1 ]
Shokouhi, Shahriar B. [1 ]
机构：
[1] Iran Univ Sci & Technol, Sch Elect Engn, Tehran, Iran
来源：
2017 25TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE) | 2017年
关键词：
Gait Recognition; Dual-Tree Complex Wavelet Transform; Gait Template; USF Dataset; DISCRIMINANT-ANALYSIS; IMAGE; FACE;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, the performance of gait recognition is improved by developing a new gait feature template. Dual-Tree Complex Wavelet Transform (DT-CWT) is used for feature template generation. DT-CWT is applied to gait images in an arbitrary decomposition level and the magnitude of the resulting six band-pass sub-images is computed. The feature template is generated by concatenating these sub-images into a single image. Furthermore, to avoid overlearning, a classifier ensemble method called Random Subspace Method (RSM) is used for classification. Experimental results on the USF database demonstrate the efficacy of the proposed model.
引用
收藏
页码：461 / 466
页数：6
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