Finger vein secure biometric template generation based on deep learning
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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.
KeywordsSecure biometric template Random projection Deep belief network
This work is supported by the National Natural Science Foundation of China (61572144), the Natural Science Foundation of Guangdong (2014A030313517), the Science and Technology Planning Project of Guangdong Province (2015B010129015, 2013B040500009), the Zhejiang Sicence Fund No. LY16F020016 and the Innovation Team Project of Guangdong Universities (No 2015KCXTD014).
Compliance with ethical standards
Conflict of interest
Yi Liu, Jie Lin, Zhusong Liu, Jian Shen and Chongzhi Gao all declare that they have no conflict of interest.
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