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An efficient face recognition algorithm based on robust principal component analysis

Authors:

Ziheng Wang,

Xudong XieAuthors Info & Claims

ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and Service

Pages 99 - 102

https://doi.org/10.1145/1937728.1937752

Published: 30 December 2010 Publication History

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Abstract

In this paper, an efficient face recognition algorithm is proposed, which is robust to illumination, expression and occlusion. In our method, a human face image is considered as a multiplication of a reflectance image and an illumination image. Then, this illumination model is used to transfer input images. After the transformation, the robust principal component analysis is employed to recover the intrinsic information of a sequence of images of one person. Finally, a new similarity metric is defined for face recognition. Experiments based on different databases illustrate that our method can achieve consistent and promising results.

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Cited By

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Lai ZBao JKong HWan MYang G(2020)Discriminative low-rank projection for robust subspace learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-020-01113-7Online publication date: 13-Mar-2020
https://doi.org/10.1007/s13042-020-01113-7
Hasban AHasif NKhan ZHusin MRashid NM. Sharif KZakaria N(2019)Face recognition for Student Attendance using Raspberry Pi2019 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE)10.1109/APACE47377.2019.9020758(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/APACE47377.2019.9020758
(2018)Incremental robust principal component analysis for face recognition using ridge regressionInternational Journal of Biometrics10.1504/IJBM.2017.0866439:3(186-204)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1504/IJBM.2017.086643
Show More Cited By

Index Terms

An efficient face recognition algorithm based on robust principal component analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
  2. Computer graphics
    1. Image manipulation

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Information & Contributors

Information

Published In

ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and Service

December 2010

218 pages

ISBN:9781450304603

DOI:10.1145/1937728

General Chairs:
Yong Rui
Microsoft China, China
,
Klara Nahrstedt
University of Illinois at Urbana-Champaign
,
Xiaofei Xu
Harbin Institute of Technology, China
,
Program Chairs:
Hongxun Yao
Harbin Institute of Technology, China
,
Shuqiang Jiang
Institute of Computing Technology, Chinese Academy of Science, China
,
Jian Cheng
Institute of Automation, Chinese Academy of Science, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 December 2010

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ICIMCS '10

ICIMCS '10: The Second International Conference on Internet Multimedia Computing and Service

December 30 - 31, 2010

Harbin, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

View all

Lai ZBao JKong HWan MYang G(2020)Discriminative low-rank projection for robust subspace learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-020-01113-7Online publication date: 13-Mar-2020
https://doi.org/10.1007/s13042-020-01113-7
Hasban AHasif NKhan ZHusin MRashid NM. Sharif KZakaria N(2019)Face recognition for Student Attendance using Raspberry Pi2019 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE)10.1109/APACE47377.2019.9020758(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/APACE47377.2019.9020758
(2018)Incremental robust principal component analysis for face recognition using ridge regressionInternational Journal of Biometrics10.1504/IJBM.2017.0866439:3(186-204)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1504/IJBM.2017.086643
Javed SOh SBouwmans TJung S(2016)Stochastic RPCA for Background/Foreground SeparationHandbook of Robust Low-Rank and Sparse Matrix Decomposition10.1201/b20190-26(457-480)Online publication date: 16-Jun-2016
https://doi.org/10.1201/b20190-26
(2016)Stochastic RPCA for Background/Foreground SeparationHandbook of Robust Low-Rank and Sparse Matrix Decomposition10.1201/b20190-21(20-1-20-24)Online publication date: 16-Jun-2016
https://doi.org/10.1201/b20190-21
Ben Thabet ABen Amor N(2015)Enhanced smart doorbell system based on face recognition2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)10.1109/STA.2015.7505106(373-377)Online publication date: Dec-2015
https://doi.org/10.1109/STA.2015.7505106
Sharma SKumar M(2014)An Occluded Face Recognition Approach using Feature Specific KPCA ApproachProceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies10.1145/2677855.2677859(1-5)Online publication date: 14-Nov-2014
https://dl.acm.org/doi/10.1145/2677855.2677859

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An Analysis on Face Recognition using Principal Component Analysis Approach

Recognition of Faces Using Improved Principal Component Analysis

Illumination-robust face recognition based on Gabor feature face intrinsic identity PCA model