Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification

Takao Murakami; Tetsushi Ohki; Yosuke Kaga; Masakazu Fujio; Kenta Takahashi

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arXiv · 1804.01670

Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification

Takao Murakami·Tetsushi Ohki·Yosuke Kaga·Masakazu Fujio·Kenta Takahashi

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Abstract

A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature represented as an image via the 2D number theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in that the transformed feature leaks no information about the original feature. However, CIRF cannot be applied to large-scale biometric identification, since the 2D inverse NTT in the matching phase requires high computational time. Furthermore, existing biometric indexing schemes cannot be used in conjunction with template protection schemes to speed up biometric identification, since a biometric index leaks some information about the original feature. In this paper, we propose a novel indexing scheme called "cancelable indexing" to speed up CIRF without losing its security properties. The proposed scheme is based on fast computation of CIRF via low-rank approximation of biometric images and via a minimum spanning tree representation of low-rank matrices in the Fourier domain. We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and thoroughly discuss the security of the proposed scheme. We also demonstrate that it significantly reduces the one-to-many matching time using a finger-vein dataset that includes six fingers from 505 subjects.

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Takao Murakami, Tetsushi Ohki, Yosuke Kaga, Masakazu Fujio, Kenta Takahashi. 2018-04-05. Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification. https://arxiv.org/abs/1804.01670

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