Abstract
The trade-off between security and verification performance is inevitable towards biometric template protection. The system developer has to sacrifice some genuine acceptance rate and tune the matching threshold to tolerating more false acceptance. To alleviate this problem, we introduce a new method of feature transformation and matching, which consists of a random undersampling and local-global matching mechanism for the hashing-based cancellable biometrics. This method manages to enlarge the gap between the mean of genuine/ impostor score distributions. As such, the decision environment is improved and the biometric system could provide more resistance to authentication attacks. Comprehensive experiments are conducted on the fingerprint FVC2002 and FVC2004 datasets, and the results demonstrate that the proposed method improves the decision environment in terms of decidability and verification performance.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 62376003, 62306003) and Anhui Provincial Natural Science Foundation (No. 2308085MF200).
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Zhou, Y., Lee, M.J., Zhang, H., Dong, X., Jin, Z. (2023). Random Undersampling and Local-Global Matching Mechanism for Cancellable Biometrics Against Authentication Attack. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_31
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DOI: https://doi.org/10.1007/978-981-99-8565-4_31
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