Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis

Soltane Mohamed

doi:doi:10.11648/j.mlr.20170201.15

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Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis

Soltane Mohamed

Published in Machine Learning Research (Volume 2, Issue 1)

Received: 12 January 2017 Accepted: 16 February 2017 Published: 3 March 2017

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Abstract

Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.

Published in	Machine Learning Research (Volume 2, Issue 1)
DOI	10.11648/j.mlr.20170201.15
Page(s)	35-50
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), 2017. Published by Science Publishing Group

Keywords

Classifier, Fusion, Biometrics, Face Verification, PCA, LDA, ICA, Likelihood Parameter Estimate

References

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Soltane Mohamed. (2017). Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Machine Learning Research, 2(1), 35-50. https://doi.org/10.11648/j.mlr.20170201.15

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ACS Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach. Learn. Res. 2017, 2(1), 35-50. doi: 10.11648/j.mlr.20170201.15

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AMA Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach Learn Res. 2017;2(1):35-50. doi: 10.11648/j.mlr.20170201.15

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@article{10.11648/j.mlr.20170201.15,
  author = {Soltane Mohamed},
  title = {Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis},
  journal = {Machine Learning Research},
  volume = {2},
  number = {1},
  pages = {35-50},
  doi = {10.11648/j.mlr.20170201.15},
  url = {https://doi.org/10.11648/j.mlr.20170201.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.15},
  abstract = {Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.},
 year = {2017}
}

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TY  - JOUR
T1  - Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis
AU  - Soltane Mohamed
Y1  - 2017/03/03
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170201.15
DO  - 10.11648/j.mlr.20170201.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 35
EP  - 50
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170201.15
AB  - Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
VL  - 2
IS  - 1
ER  -

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Author Information

Soltane Mohamed

Electrical Engineering & Computing Department, Faculty of Sciences & Technology, Doctor Yahia Fares University of Medea, Medea, Algeria

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APA Style

Soltane Mohamed. (2017). Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Machine Learning Research, 2(1), 35-50. https://doi.org/10.11648/j.mlr.20170201.15

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ACS Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach. Learn. Res. 2017, 2(1), 35-50. doi: 10.11648/j.mlr.20170201.15

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AMA Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach Learn Res. 2017;2(1):35-50. doi: 10.11648/j.mlr.20170201.15

Copy | Download

@article{10.11648/j.mlr.20170201.15,
  author = {Soltane Mohamed},
  title = {Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis},
  journal = {Machine Learning Research},
  volume = {2},
  number = {1},
  pages = {35-50},
  doi = {10.11648/j.mlr.20170201.15},
  url = {https://doi.org/10.11648/j.mlr.20170201.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.15},
  abstract = {Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis
AU  - Soltane Mohamed
Y1  - 2017/03/03
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170201.15
DO  - 10.11648/j.mlr.20170201.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 35
EP  - 50
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170201.15
AB  - Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
VL  - 2
IS  - 1
ER  -

Copy | Download