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), 2024. Published by Science Publishing Group

Keywords

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

References

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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