Multi-View Feature Selection for Heterogeneous Face Recognition

被引：9

作者：

Gui, Jie ^{[1
]}

Li, Ping ^{[2
]}

机构：

[1] Rutgers State Univ, Dept Stat & Biostat, Piscataway, NJ 08854 USA

[2] Baidu Res, Bellevue, WA 98004 USA

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) | 2018年

关键词：

multi-view feature selection; multi-view discriminant analysis; heterogeneous face recognition; IMAGING GENETICS;

D O I：

10.1109/ICDM.2018.00122

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

While the task of feature selection has been studied for many years, the topic of multi-view feature selection for heterogeneous face recognition (HFR) such as visible (VIS) image versus near infrared (NIR) image recognition, photo versus sketch recognition, and face recognition across pose, is rarely studied. In this paper, we propose a multi-view feature selection method (MvFS) for HFR. To the best of our knowledge, MvFS is the first algorithm to address the problem of multiview feature selection for HFR, in which the dimensionalities of different views are the same and the number of selected features of different views are the same. The proposed algorithm is simple and computationally efficient. Our experiments confirm the effectiveness of MvFS.(1)

引用

页码：983 / 988

页数：6

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