Finger Vein Template Protection Based on Alignment-Robust Feature Description and Index-of-Maximum Hashing

Kirchgasser, Simon; Kauba, Christof; Lai, Yen-Lung; Jin, Zhe; Uhl, Andreas

doi:10.1109/tbiom.2020.2981673

Cited by 30 publications

(19 citation statements)

References 52 publications

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“…Regarding unlinkability, our proposal outperforms the results obtained by using re-mapping, warping and Alignment-Robust Hashing proposals included in [8]. The rest of the proposals do not provide unlinkability results.…”

Section: Security Analysis Of the Protected Approachmentioning

confidence: 74%

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A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Arjona

Baturone

2020

IEEE Access

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Biometric recognition has the potential to authenticate individuals by an intrinsic link between the individual and their physical, physiological and/or behavioral characteristics. This leads a higher security level than the authentication solely based on knowledge or possession. One of the reasons why biometrics is not completely accepted is the lack of trust in the storage of biometric templates in external servers. Biometric data are sensitive data which should be protected as is contemplated in the data protection regulation of many countries. In this work, we propose the use of biometric Learning Parity With Noise (LPN) commitments as template protection scheme. To the best of our knowledge, this is the first proposal for biometric template protection based on the LPN problem (that is, the difficulty of decoding random linear codes), which offers post-quantum security. Biometric features are compared in the protected domain. Irreversibility, revocability, and unlinkability properties are satisfied as well as resistance to False Acceptance Rate (FAR), crossmatching, Stolen Token, and similarity-based attacks. A recognition accuracy with a 0% FAR is achieved, because user-specific secret keys are employed, and the False Rejection Ratio (FRR) can be adjusted depending on a threshold to preserve the accuracy of the unprotected scheme in the Stolen Token scenario. A good performance in terms of execution time, template storage and operation complexity is obtained for security levels at least of 80 bits. The proposed scheme is employed in a dual-factor authentication protocol from the literature to illustrate how it provides security using authentication and database (cloud) servers that can be malicious. The proposed LPN-based protected scheme can be applied to any biometric trait represented by binary features and any matching score based on Hamming or Jaccard distances. In particular, experimental results are included of a practical finger vein-based recognition system implemented in Matlab.

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Section: Security Analysis Of the Protected Approachmentioning

confidence: 74%

“…Transformations can be non-invertible or invertible (salting). Transformation functions proposed in the literature are BioHashing [7], Alignment-Robust Hashing (ARH) [8], re-mapping and warping [9], and Bloom filters [10]. Unlinkability and revocability are based on the variation of the parameters of the transformation functions.…”

Section: Introductionmentioning

confidence: 99%

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Arjona

Baturone

2020

IEEE Access

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“…Kirchgasser et al [27] proposed a cancellable template protection scheme based on index-of-maximum (IoM) hashing to protect a finger vein template. They use six methods to extract finger vein features: Gabor filter (GF), isotropic undecimated wavelet transform (IUWT), maximum curvature (MC), principal curvature (PC), repeated line tracking (RLT), and wide line detector (WLD).…”

Section: Related Workmentioning

confidence: 99%

Biometric Template Protection for Dynamic Touch Gestures Based on Fuzzy Commitment Scheme and Deep Learning

Bajaber

Elrefaei

2022

Mathematics

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Privacy plays an important role in biometric authentication systems. Touch authentication systems have been widely used since touch devices reached their current level of development. In this work, a fuzzy commitment scheme (FCS) is proposed based on deep learning (DL) to protect the touch-gesture template in a touch authentication system. The binary Bose–Ray-Chaudhuri code (BCH) is used with FCS to deal with touch variations. The BCH code is described by the triplet (n, k, t) where n denotes the code word’s length, k denotes the length of the key and t denotes error-correction capability. In our proposed system, the system performance is investigated using different lengths k. The learning-based approach is applied to extract touch features from raw touch data, as the recurrent neural network (RNN) is used based on a convolutional neural network (CNN). The proposed system has been evaluated on two different touch datasets: the Touchalytics dataset and BioIdent dataset. The best results obtained were with a key length k = 99 and n = 255; the false accept rate (FAR) was 0.00 and false reject rate (FRR) was 0.5854 for the Touchalytics dataset, while the FAR was 0.00 and FRR was 0.5399 with the BioIdent dataset. The FCS shows its effectiveness in dynamic authentication systems, as good results are obtained and compared with other works.

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“…In the context of vascular biometrics several recent approaches exist that can be applied to protect the biometric templates. We will not discuss them here in detail, but the interested reader is referred to [35], [48] for studies on CB and [10], [15], [22], [64] where various BCS are proposed and evaluated.…”

Section: Related Workmentioning

confidence: 99%

Inverse Biometrics: Generating Vascular Images From Binary Templates

Kauba

Kirchgasser

Mirjalili

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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In this work, we investigate the possibility of generating grayscale images of finger and hand vein patterns from their corresponding binary templates. This exercise would allow us to determine the invertibility of vascular templates, which has implications in biometric security and privacy. The transformation from binary features to a gray-scale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger-vein datasets and 3 hand-vein datasets. Results indicate that (a) it is possible to reconstruct the considered vascular images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization). The results of this research further underscore the need for properly securing biometric templates, even if they are of binary nature.

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Finger Vein Template Protection Based on Alignment-Robust Feature Description and Index-of-Maximum Hashing

Cited by 30 publications

References 52 publications

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Biometric Template Protection for Dynamic Touch Gestures Based on Fuzzy Commitment Scheme and Deep Learning

Inverse Biometrics: Generating Vascular Images From Binary Templates

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