A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

Yang, Ziyuan; Kang, Ming; Teoh, Andrew Beng Jin; Gao, Chengrui; Chen, Wen; Zhang, Bob; Zhang, Yi

doi:10.1109/tifs.2024.3461869

IEEE Trans.Inform.Forensic Secur.

2024

DOI: 10.1109/tifs.2024.3461869

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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

Ziyuan Yang,

Ming Kang,

Andrew Beng Jin Teoh

et al.

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Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

Ali,

Elhefnawy,

Abo-Zahhad

2024

EURASIP J. on Info. Security

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Cancelable template protection techniques are indispensable to provide essential security and privacy privileges in biometric systems. This paper introduces an efficient cancelable palmprint recognition technique based on multi-level transformations. Gabor filtering with feature remapping is introduced for extracting highly discriminative features. Histogram remapping is applied to nonlinearly transform the downsampled Gabor features to be normally distributed. This feature-remapping step is proposed to enhance the discriminatory power and alleviate the effect of feature variability and image artifacts. Comb filtering is applied to the mapped features as a first protection layer. To provide security guarantees against linkability attacks, index-based locality-sensitive hashing (LSH) is introduced as a second protection layer to transform the comb-filtered mapped real-valued features into maximum-ranked indices. Recognition is performed in the secured domain to accelerate matching and to preserve the user’s privacy. Results show that the proposed scheme provides a large re-issuance ability, protects templates from being inverted, and demonstrates strong unlinkability across different databases. In addition, favorable verification/identification accuracy is obtained and the system satisfies the needs for real-time applications. A global measure $${D}_{\leftrightarrow }^{sys}$$ D ↔ sys value of 0.01 is obtained, and thus, correlation attacks are mitigated. The recognition results for the legitimate scenario are 100% identification accuracy and 0% EER. For the worst case (same token scenario), the corresponding results are 99.752% and 0.31%, respectively.

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Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

Ali,

Elhefnawy,

Abo-Zahhad

2024

EURASIP J. on Info. Security

View full text Add to dashboard Cite

show abstract

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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

Cited by 1 publication

References 66 publications

Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

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