A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems
It is already a consensus that intra-class variations will decline the performance of biometric systems. Many studies have explained how intra-class variations affect the biometric systems, while few approaches have proposed the effective solutions of template update. Since template update is a cruc...
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IEEE
2019-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/8854794/ |
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author | Xi Chen Ming Yu Feng Yue Bin Li Yingchun Guo |
author_facet | Xi Chen Ming Yu Feng Yue Bin Li Yingchun Guo |
author_sort | Xi Chen |
collection | DOAJ |
description | It is already a consensus that intra-class variations will decline the performance of biometric systems. Many studies have explained how intra-class variations affect the biometric systems, while few approaches have proposed the effective solutions of template update. Since template update is a crucial procedure in biometric recognition systems, the problem is challenging because the newly selected templates must be representative of a large amount of intra-class variations such as posture changes and lighting conditions. To this end, existing techniques proposed to perform template update by periodically selecting new representative templates. However, all of them are designed for specific biometric traits rather a generic framework, and they are either ineffective because selected templates are less representative or prone to selection errors due to the presence of outliers. In this paper, we propose a novel generic template update framework. We use pass tables to accumulate information for outlier removal and representative templates selection, and further, propose a novel criterion to select the optimal template set according to current templates and similarity threshold. To confirm the effectiveness of our method, we carry out several experiments on synthetic and real datasets from different biometric traits. The results show that the proposed method is more robust than existing ones, and at the same time, achieves a higher accuracy according to the specified performance indicator. It also demonstrated the superiority of our method to random selection for different biometric traits. |
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format | Article |
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institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-04-12T04:56:52Z |
publishDate | 2019-01-01 |
publisher | IEEE |
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series | IEEE Access |
spelling | doaj.art-6ce91fe93b0643b6905e701541a2eefe2022-12-22T03:47:06ZengIEEEIEEE Access2169-35362019-01-01714508214509410.1109/ACCESS.2019.29450648854794A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric SystemsXi Chen0https://orcid.org/0000-0003-1205-2222Ming Yu1Feng Yue2Bin Li3Yingchun Guo4https://orcid.org/0000-0002-3239-3086School of Electronic and Information Engineering, Hebei University of Technology (HEBUT), Tianjin, ChinaSchool of Electronic and Information Engineering, Hebei University of Technology (HEBUT), Tianjin, ChinaBeijing Institute of New Technology Applications, Beijing Academy of Sciences and Technology, Beijing, ChinaBeijing Institute of New Technology Applications, Beijing Academy of Sciences and Technology, Beijing, ChinaSchool of Artificial Intelligence, Hebei University of Technology (HEBUT), Tianjin, ChinaIt is already a consensus that intra-class variations will decline the performance of biometric systems. Many studies have explained how intra-class variations affect the biometric systems, while few approaches have proposed the effective solutions of template update. Since template update is a crucial procedure in biometric recognition systems, the problem is challenging because the newly selected templates must be representative of a large amount of intra-class variations such as posture changes and lighting conditions. To this end, existing techniques proposed to perform template update by periodically selecting new representative templates. However, all of them are designed for specific biometric traits rather a generic framework, and they are either ineffective because selected templates are less representative or prone to selection errors due to the presence of outliers. In this paper, we propose a novel generic template update framework. We use pass tables to accumulate information for outlier removal and representative templates selection, and further, propose a novel criterion to select the optimal template set according to current templates and similarity threshold. To confirm the effectiveness of our method, we carry out several experiments on synthetic and real datasets from different biometric traits. The results show that the proposed method is more robust than existing ones, and at the same time, achieves a higher accuracy according to the specified performance indicator. It also demonstrated the superiority of our method to random selection for different biometric traits.https://ieeexplore.ieee.org/document/8854794/Intra-class variationspass tabletemplate selectiontemplate update |
spellingShingle | Xi Chen Ming Yu Feng Yue Bin Li Yingchun Guo A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems IEEE Access Intra-class variations pass table template selection template update |
title | A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems |
title_full | A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems |
title_fullStr | A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems |
title_full_unstemmed | A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems |
title_short | A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems |
title_sort | generic template update framework the solution for intra class variations in biometric systems |
topic | Intra-class variations pass table template selection template update |
url | https://ieeexplore.ieee.org/document/8854794/ |
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