Enhanced multi-line code for minutiae-based fingerprint template protection

Wong, Wei Jing; Teoh, Andrew Beng Jin; Wong, M. L. Dennis; Kho, Yau Hee

doi:10.1016/j.patrec.2013.03.039

Cited by 58 publications

(32 citation statements)

References 24 publications

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“…Multi-line Code (MLC) proposed by Wong et al [14] is a minutia descriptor constructed based on the spatial distribution of the neighboring minutiae within a fixed radius. Firstly, a straight line is drawn following the direction of the reference minutiae and a number of overlapped circles with a predefined radius can be constructed.…”

Section: A Minutiae-based Fingerprint Cancelable Templatementioning

confidence: 99%

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Jin

Hwang

Lai

et al. 2018

IEEE Trans.Inform.Forensic Secur.

184

101

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Abstract-In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy several merits. Firstly, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.Index Terms-Fingerprint, cancelable template, Index-of-Max hashing, security and privacy I. INTRODUCTIONATELY, rapid proliferation of biometric applications leads to massive amount of biometric templates. Public worries about the security and privacy of the biometric templates if stolen or compromised. Such concerns are attributed to the strong binding of individuals and privacy, and further complicated by the fact that biometric traits are irrevocable. Given the above threats, a number of proposals have been reported to protect the biometric templates. Generally, the proposals available in the literature can be broadly divided into two categories: feature transformation (or cancelable biometrics) and biometric cryptosystems (biometric encryption). Biometric cryptosystem serves the purpose of either securing the secret using biometric feature (key binding) or generating the secret directly from the biometric feature (key generation). On the other hand, cancelable biometrics [1] is truly meant for biometric template protection. It refers to the irreversible transform that can alter the biometric templates such that security and privacy of the templates can be assured. If a cancelable template is compromised, a new template can be re-generated from the same biometrics.The cancelable biometric schemes in the literature vary according to different biometric modalities. However, the operation of a general cancelable biometrics system is similar to the conventional biometric system where the system composes of sensor, feature extractor and matcher except the former includes a parameterized transformation function right after feature extractor, and the matching is done in the transformed domain, rather in the feature doma...

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Section: A Minutiae-based Fingerprint Cancelable Templatementioning

confidence: 99%

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Jin

Hwang

Lai

et al. 2018

IEEE Trans.Inform.Forensic Secur.

184

101

View full text Add to dashboard Cite

show abstract

“…Jin et al proposed fingerprint template protection with a minutiaebased bit string for security and preservation of privacy [4]. Wong et al calculated the mean distance of minutiae that fall in every semicircle region along the lines, stored the mean distance sequentially, and performed quantization to convert it into a binary string [5]. Iloanusi introduced a multifinger indexing method using minutiae quadruplets in combination with a clustering scheme [6].…”

Section: Related Workmentioning

confidence: 99%

“…The most popular fingerprint recognition method today is the minutiae-based method [2][3][4][5][6][7][8]. It recognizes fingerprints using character information including endings of fingerprint ridges and ridge bifurcation points.…”

Section: Introductionmentioning

confidence: 99%

Low-quality fingerprint recognition using a limited ellipse-band-based matching method

Zhao

Zhang

2015

J. Opt. Soc. Am. A

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Current fingerprint recognition technologies are based mostly on the minutia algorithms, which cannot recognize fingerprint images in low-quality conditions. This paper proposes a novel recognition algorithm using a limited ellipse-band-based matching method. It uses the Fourier-Mellin transformation method to improve the limitation of the original algorithm, which cannot resist rotation changes. Furthermore, an ellipse band on the frequency amplitude is used to suppress noise that is introduced by the high-frequency parts of images. Finally, the recognition result is obtained by considering both the contrast and position correlation peaks. The experimental results show that the proposed algorithm can increase the recognition accuracy, particularly of images in low-quality conditions.

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“…In minutiae-based matching, each minutia in the minutiae set can be transformed into a high-dimensional vector using minutia descriptor [1], [17]. These vectors are hereafter referred as minutia vectors.…”

Section: Problem Formulationmentioning

confidence: 99%

Minutiae set to bit-string conversion using multi-scale bag-of-words paradigm

Wong

Kho

et al. 2014

2014 IEEE International Workshop on Information Forensics and Security (WIFS)

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Minutiae-based matching is commonly used in fingerprint recognition systems due to its proven performance. However, such matching procedure usually involves unordered and variable size templates and it does not favour emerging bio-cryptography applications and most classifiers. This paper proposes a solution by converting the original minutiae set into a bit-string through the amalgamation of bag-of-words modelling, multi-scale construction and dynamic quantization. Experimental results show that the proposed method has high potential in biocryptography applications due to its outstanding EER of ≤ 0.51% and entropy of 723 bits. Further security and privacy concerns are also analyzed.

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Enhanced multi-line code for minutiae-based fingerprint template protection

Cited by 58 publications

References 24 publications

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Low-quality fingerprint recognition using a limited ellipse-band-based matching method

Minutiae set to bit-string conversion using multi-scale bag-of-words paradigm

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