Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization

Yang Hongli; Hu Yunhong

doi:doi:10.11648/j.ajsea.20150403.12

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Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization

Yang Hongli, Hu Yunhong

Published in American Journal of Software Engineering and Applications (Volume 4, Issue 3)

Received: 21 April 2015 Accepted: 1 May 2015 Published: 13 May 2015

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Abstract

Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.

Published in	American Journal of Software Engineering and Applications (Volume 4, Issue 3)
DOI	10.11648/j.ajsea.20150403.12
Page(s)	50-55
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Local Feature Extraction, Incomplete Data, Face Recognition, NMF, Model

References

[1]	Hongli Yang. Nonnegative matrix and tensor factorization and their applications. Ph.D thesis, 2011.
[2]	Y. D. Kim and S. Choi. Weighted nonnegative matrix factorization. ICASSP 2009: 1541-1544.
[3]	H.Lee,J.Yoo and S.Choi. Semi-supervised nonnegative matrix factorization IEEE Signal Processing Letters, 2010, Vol (17) (1): 4-7.
[4]	S. Zhang W. H. Wang, J. Ford and F. Makedon. Learning from incomplete ratings using nonnegative matrix factorization. SIGCOMM 2006: 267-278.
[5]	E. Acar, D. M. Dunlavy, T. G. Kolda, and M. Morup. Scalable tensor factorization with missing data. Proceedings of the 2010 SIAM International Conference on Data Mining, 2010.
[6]	N. Srebro,T. Jaakkola. Weighted low rank approximation. IMCL2003: 720-727.
[7]	P. Paatero. Least squares formulation of robust, nonnegative factor analysis. Chemometrics and Intelligent Laboratory Systems, 1997, Vol (37) (1): 23-35.
[8]	A. M. Buchanan and A. W. Fitzgibbon. Damped Newton algorithms for matrix factorization with missing data CVPR2005, Vol (2): 316-322
[9]	V. D. Blondel, N. D. Ho, and P. V. Dooren. Weighted nonnegative matrix factorization and face feature extraction. Image and Vision Comput-ing, 2008.
[10]	G. Tomasi and R. Bro. Parafac and missing values. Chemometrics and intelligent laboratory systems.2005, Vol (75) (2): 163-180.
[11]	A. P. Dempster, N. M. Laird and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society, 1977, Vol (39) (1): 1-38.
[12]	E. J. Candes and Y. Plan. Matrix completion with noise.arXiv: 0903.3131v1vl, 2009.
[13]	E. J. Candes and T. Tao. The power of convex relaxation: near-optimal matrix completion.arXiv:0903.1476vl,2009.
[14]	K. R. Gabriel and S. Zamir. Lower rank approximation of matrices by least squares approximation with any choice of weights. Technimetrics, 1979, Vol (21)(4): 489-498.
[15]	A. Ruhe. Numerical computation of principal components when several observations are missing. Technical report, University of Umea, Sweden, 1974.
[16]	R.J.A.Little and D.B.Rubin. The analysis of social science data with missing values. Sociological Methods and Research,1989,Vol(18)(2-3):292-326.
[17]	R. L. Carter. Solutions for missing data in structural equation modeling. Research and Practice in assessment, 2006, Vol (1)(1): 1-6.
[18]	D. Guillamet, J. Vitria and B. Schiele. Introducing a weighted nonnegative matrix factorization for image classification. Pattern Recognition Letters, 2003, Vol (24)(14): 2447-2454.
[19]	X. Li and K. Fukui. Fisher nonnegative factorization with pair wise weighting. MVA 2007, IAPR: 380-383.
[20]	P. J. B. Koeck. Missing data in image and signal processing: the case of binary objects. International journal for light and electron optics, 2004, Vol (115)(10): 459-472.
[21]	R. B. Kline. Principles and practices of structural equation modeling. Third edition, John Wiley &Sons, Inc., New York, 1988.
[22]	D. D. Lee and H. S. Seung. Learning the parts of objects by nonnegative matrix factorization. Nature, 1999, Vol (401)(6755): 788-794.

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APA Style

Yang Hongli, Hu Yunhong. (2015). Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization. American Journal of Software Engineering and Applications, 4(3), 50-55. https://doi.org/10.11648/j.ajsea.20150403.12

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ACS Style

Yang Hongli; Hu Yunhong. Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization. Am. J. Softw. Eng. Appl. 2015, 4(3), 50-55. doi: 10.11648/j.ajsea.20150403.12

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AMA Style

Yang Hongli, Hu Yunhong. Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization. Am J Softw Eng Appl. 2015;4(3):50-55. doi: 10.11648/j.ajsea.20150403.12

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@article{10.11648/j.ajsea.20150403.12,
  author = {Yang Hongli and Hu Yunhong},
  title = {Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization},
  journal = {American Journal of Software Engineering and Applications},
  volume = {4},
  number = {3},
  pages = {50-55},
  doi = {10.11648/j.ajsea.20150403.12},
  url = {https://doi.org/10.11648/j.ajsea.20150403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20150403.12},
  abstract = {Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we  provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.},
 year = {2015}
}

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TY  - JOUR
T1  - Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization
AU  - Yang Hongli
AU  - Hu Yunhong
Y1  - 2015/05/13
PY  - 2015
N1  - https://doi.org/10.11648/j.ajsea.20150403.12
DO  - 10.11648/j.ajsea.20150403.12
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 50
EP  - 55
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20150403.12
AB  - Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we  provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.
VL  - 4
IS  - 3
ER  -

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Author Information

Yang Hongli

Science College, Shandong University of Science and Technology, Qingdao, Shandong, P. R. China
Hu Yunhong

Applied Mathematics Department, Yuncheng University, Yuncheng, P. R. China

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APA Style

Yang Hongli, Hu Yunhong. (2015). Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization. American Journal of Software Engineering and Applications, 4(3), 50-55. https://doi.org/10.11648/j.ajsea.20150403.12

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ACS Style

Yang Hongli; Hu Yunhong. Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization. Am. J. Softw. Eng. Appl. 2015, 4(3), 50-55. doi: 10.11648/j.ajsea.20150403.12

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AMA Style

Yang Hongli, Hu Yunhong. Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization. Am J Softw Eng Appl. 2015;4(3):50-55. doi: 10.11648/j.ajsea.20150403.12

Copy | Download

@article{10.11648/j.ajsea.20150403.12,
  author = {Yang Hongli and Hu Yunhong},
  title = {Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization},
  journal = {American Journal of Software Engineering and Applications},
  volume = {4},
  number = {3},
  pages = {50-55},
  doi = {10.11648/j.ajsea.20150403.12},
  url = {https://doi.org/10.11648/j.ajsea.20150403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20150403.12},
  abstract = {Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we  provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization
AU  - Yang Hongli
AU  - Hu Yunhong
Y1  - 2015/05/13
PY  - 2015
N1  - https://doi.org/10.11648/j.ajsea.20150403.12
DO  - 10.11648/j.ajsea.20150403.12
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 50
EP  - 55
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20150403.12
AB  - Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we  provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.
VL  - 4
IS  - 3
ER  -

Copy | Download