Multi‐view intrinsic low‐rank representation for robust face recognition and clustering-Reference-Cited by-同舟云学术

Multi‐view intrinsic low‐rank representation for robust face recognition and clustering

Published:2021-05-02 Issue:14 Volume:15 Page:3573-3584
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Wang Zhi‐yang¹^ORCID,Abhadiomhen Stanley Ebhohimhen¹²,Liu Zhi‐feng¹³,Shen Xiang‐jun¹,Gao Wen‐yun⁴⁵,Li Shu‐ying⁶

Affiliation:

1. School of Computer Science and Communication Engineering JiangSu University Zhenjiang JiangSu China

2. Department of Computer Science University of Nigeria Nsukka Nigeria

3. Jingkou New‐Generation Information Technology Industry Institute Jiangsu University Zhenjiang Jiangsu China

4. Nanjing LES Information Technology Co., LTD Nanjing JiangSu China

5. College of Computer and Information Hohai University Nanjing JiangSu China

6. School of Automation Xi'an University of Posts & Telecommunications Xi'an Shaanxi China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12232

Reference43 articles.

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3. Rattani A. Tistarelli M.:Robust multi‐modal and multi‐unit feature level fusion of face and iris biometrics. In:International Conference on Biometrics pp.960‐969.Springer Berlin Heidelberg(2009)

4. Kumar A. et al.:Co‐regularized multi‐view spectral clustering. In:Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011 NIPS 2011.ACM New York(2011)

5. Xia R. et al.:Robust multi‐view spectral clustering via low‐rank and sparse decomposition. In:Proceedings of the National Conference on Artificial Intelligence.AAAI Press Palo Alto(2014)

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