Comparison of combination methods utilizing T-normalization and second best score model

Sergey Tulyakov; Zhi Zhang; Venu Govindaraju

doi:10.1109/CVPRW.2008.4563105

Comparison of combination methods utilizing T-normalization and second best score model

Sergey Tulyakov
, Zhi Zhang
, Venu Govindaraju

Research and Economic Development

SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 Scopus citations

Abstract

The combination of biometric matching scores can be enhanced by taking into account the matching scores related to all enrolled persons in addition to traditional combinations utilizing only matching scores related to a single person. Identification models take into account the dependence between matching scores assigned to different persons and can be used for such enhancement. In this paper we compare the use of two such models - T-normalization and second best score model. The comparison is performed using two combination algorithms - likelihood ratio and multilayer perceptron. The results show, that while second best score model delivers better performance improvement than T-normalization, two models are complementary to each other and can be used together for further improvements.

Original language	English
Title of host publication	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
DOIs	https://doi.org/10.1109/CVPRW.2008.4563105
State	Published - 2008
Event	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops - Anchorage, AK, United States Duration: Jun 23 2008 → Jun 28 2008

Publication series

Name	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Conference

Conference	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
Country/Territory	United States
City	Anchorage, AK
Period	06/23/08 → 06/28/08

Access to Document

10.1109/CVPRW.2008.4563105

Cite this

Tulyakov, S., Zhang, Z., & Govindaraju, V. (2008). Comparison of combination methods utilizing T-normalization and second best score model. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops Article 4563105 (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops). https://doi.org/10.1109/CVPRW.2008.4563105

@inproceedings{59717057fbb64480ac67537cce5f400a,

title = "Comparison of combination methods utilizing T-normalization and second best score model",

abstract = "The combination of biometric matching scores can be enhanced by taking into account the matching scores related to all enrolled persons in addition to traditional combinations utilizing only matching scores related to a single person. Identification models take into account the dependence between matching scores assigned to different persons and can be used for such enhancement. In this paper we compare the use of two such models - T-normalization and second best score model. The comparison is performed using two combination algorithms - likelihood ratio and multilayer perceptron. The results show, that while second best score model delivers better performance improvement than T-normalization, two models are complementary to each other and can be used together for further improvements.",

author = "Sergey Tulyakov and Zhi Zhang and Venu Govindaraju",

year = "2008",

doi = "10.1109/CVPRW.2008.4563105",

language = "English",

isbn = "9781424423408",

series = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops",

booktitle = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops",

note = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops ; Conference date: 23-06-2008 Through 28-06-2008",

}

Tulyakov, S, Zhang, Z & Govindaraju, V 2008, Comparison of combination methods utilizing T-normalization and second best score model. in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops., 4563105, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, Anchorage, AK, United States, 06/23/08. https://doi.org/10.1109/CVPRW.2008.4563105

Comparison of combination methods utilizing T-normalization and second best score model. / Tulyakov, Sergey; Zhang, Zhi; Govindaraju, Venu.
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. 4563105 (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - The combination of biometric matching scores can be enhanced by taking into account the matching scores related to all enrolled persons in addition to traditional combinations utilizing only matching scores related to a single person. Identification models take into account the dependence between matching scores assigned to different persons and can be used for such enhancement. In this paper we compare the use of two such models - T-normalization and second best score model. The comparison is performed using two combination algorithms - likelihood ratio and multilayer perceptron. The results show, that while second best score model delivers better performance improvement than T-normalization, two models are complementary to each other and can be used together for further improvements.

AB - The combination of biometric matching scores can be enhanced by taking into account the matching scores related to all enrolled persons in addition to traditional combinations utilizing only matching scores related to a single person. Identification models take into account the dependence between matching scores assigned to different persons and can be used for such enhancement. In this paper we compare the use of two such models - T-normalization and second best score model. The comparison is performed using two combination algorithms - likelihood ratio and multilayer perceptron. The results show, that while second best score model delivers better performance improvement than T-normalization, two models are complementary to each other and can be used together for further improvements.

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DO - 10.1109/CVPRW.2008.4563105

M3 - Conference contribution

AN - SCOPUS:52049090335

SN - 9781424423408

T3 - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

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T2 - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

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Tulyakov S, Zhang Z, Govindaraju V. Comparison of combination methods utilizing T-normalization and second best score model. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. 4563105. (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops). doi: 10.1109/CVPRW.2008.4563105

Comparison of combination methods utilizing T-normalization and second best score model

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