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Soft Computing

Erschienen in:

21.01.2017 | Methodologies and Application

Finger vein secure biometric template generation based on deep learning

verfasst von: Yi Liu, Jie Ling, Zhusong Liu, Jian Shen, Chongzhi Gao

Erschienen in: Soft Computing | Ausgabe 7/2018

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Abstract

Leakage of unprotected biometric authentication data has become a high-risk threat for many applications. Lots of researchers are investigating and designing novel authentication schemes to prevent such attacks. However, the biggest challenge is how to protect biometric data while keeping the practical performance of identity verification systems. For the sake of tackling this problem, this paper presents a novel finger vein recognition algorithm by using secure biometric template scheme based on deep learning and random projections, named FVR-DLRP. FVR-DLRP preserves the core biometric information even with the user’s password cracked, whereas the original biometric information is still safe. The results of experiment show that the algorithm FVR-DLRP can maintain the accuracy of biometric identification while enhancing the uncertainty of the transformation, which provides better protection for biometric authentication.

Vorheriger Artikel Evolutionary trust scheme of certificate game in mobile cloud computing

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Titel: Finger vein secure biometric template generation based on deep learning
verfasst von: Yi Liu
Jie Ling
Zhusong Liu
Jian Shen
Chongzhi Gao
Publikationsdatum: 21.01.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 7/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2487-9

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