Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods

Min Yi Jeong, Jeung Yoon Choi, Jaihie Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.

Original languageEnglish
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
Publication statusPublished - 2007 Oct 11
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: 2007 Jun 172007 Jun 22

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period07/6/1707/6/22

Fingerprint

Independent component analysis
Biometrics
Genetic algorithms
Authentication
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Jeong, M. Y., Choi, J. Y., & Kim, J. (2007). Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270382] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383384
Jeong, Min Yi ; Choi, Jeung Yoon ; Kim, Jaihie. / Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods. 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
@inproceedings{58b0e9d82a5d42a980c8b52fa992585a,
title = "Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods",
abstract = "Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.",
author = "Jeong, {Min Yi} and Choi, {Jeung Yoon} and Jaihie Kim",
year = "2007",
month = "10",
day = "11",
doi = "10.1109/CVPR.2007.383384",
language = "English",
isbn = "1424411807",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
booktitle = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07",

}

Jeong, MY, Choi, JY & Kim, J 2007, Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods. in 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07., 4270382, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07, Minneapolis, MN, United States, 07/6/17. https://doi.org/10.1109/CVPR.2007.383384

Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods. / Jeong, Min Yi; Choi, Jeung Yoon; Kim, Jaihie.

2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270382 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods

AU - Jeong, Min Yi

AU - Choi, Jeung Yoon

AU - Kim, Jaihie

PY - 2007/10/11

Y1 - 2007/10/11

N2 - Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.

AB - Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.

UR - http://www.scopus.com/inward/record.url?scp=34948825888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34948825888&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2007.383384

DO - 10.1109/CVPR.2007.383384

M3 - Conference contribution

AN - SCOPUS:34948825888

SN - 1424411807

SN - 9781424411801

T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

BT - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07

ER -

Jeong MY, Choi JY, Kim J. Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270382. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383384