Binary feature vector fingerprint representation from minutiae vicinities

Bringer, Julien; Despiegel, Vincent

doi:10.1109/btas.2010.5634488

Cited by 64 publications

(41 citation statements)

References 29 publications

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“…So, there has to be an optimal value for number of clusters for which the accuracy is maximized, in our experiments we observed that the accuracy was maximum for 1000 clusters. The results have been compared (see Figure 7) with spectral minutiae representation [12] and binary representation through minutiae vicinities [6]. These are the two major fixed-length quantized fingerprint representations in the literature.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Also, a minutiae based representation due to its variable size is not suitable for recently proposed template protection schemes such as [3] and [5]. Bringer in his work [6], transforms a minutiae set into a fixed-length quantized feature vector by matching small minutiae vicinities (or neighborhoods) with a set of representative vicinities.…”

Section: Introductionmentioning

confidence: 99%

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Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints

Vij

Namboodiri

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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Representation of fingerprints is one of the key factors that limits the accuracy and efficiency of matching algorithms. Most popular methods represent each fingerprint as an unordered set of minutiae with variable cardinality and the matching algorithms are left with the task of finding the best correspondence between the two sets of minutiae. While this makes the representation more flexible and matching more accurate, the task becomes computationally intensive. Fixed length representations with aligned features are highly efficient to match. However, creating an aligned representation without the knowledge of the sample to which it is to be matched, makes the problem of representation more complex. Some of the fixed-length representations only provide partial alignment, leaving the rest to the matching stage. In this paper, we propose a fixed length representation for fingerprints that provides exact alignment between the features, thus enabling high-speed matching with minimal computational effort. The representation extends the idea of object representation using bag of words into a bag of minutiae neighborhoods. The representation is provably invariant to affine transformations (rotation, translation and uniform scaling), and is shown to be highly discriminative for the task of verification. Experimental results on FVC 2002 and 2004 datasets clearly show the superiority of the representation with competing methods. As the proposed representation can be computed from the standard minutiae templates, the method is applicable to existing datasets, where the original fingerprint images are not available.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints

Vij

Namboodiri

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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“…So far, hardly any suggestions have been made to construct align-invariant BCSs or CB. Feature adaptation schemes that preserve accuracy have to be utilized in order to obtain common representations of arbitrary biometric characteristics (several approaches to extract binary fingerprint templates have been proposed, e.g., [211,212]) allowing biometric fusion in a form suitable for distinct template protection schemes. In addition, several suggestions for protocols providing provable secure biometric authentication based on template protection schemes have been made [150,192,213,214].…”

Section: F Open Issues and Challengesmentioning

confidence: 99%

A survey on biometric cryptosystems and cancelable biometrics

Rathgeb

Uhl

2011

EURASIP J. on Info. Security

505

337

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Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.

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“…For iriscode biometric data, the comparison of two iriscodes is made thanks to the computation of an Hamming distance [4]. There is today a trend to generalize this way of performing biometric matching for other modalities [5,6] for easier embedding into cryptographic protocols. In their works on private identification, Bringer et al [7][8][9] (see also Section Private identification schemes) actually show how to carry out fuzzy keyword search for the Hamming distance.…”

Section: Related Workmentioning

confidence: 99%

Embedding edit distance to enable private keyword search

Bringer¹,

Chabanne

2012

Human-centric Computing and Information Sciences

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Background: Our work is focused on fuzzy keyword search over encrypted data in Cloud Computing. Methods: We adapt results on private identification schemes by Bringer et al. to this new context. We here exploit a classical embedding of the edit distance into the Hamming distance. Results: Our way of doing enables some flexibility on the tolerated edit distance when looking for close keywords while preserving the confidentiality of the queries. Conclusion: Our proposal is proved secure in a security model taking into account privacy.

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Binary feature vector fingerprint representation from minutiae vicinities

Cited by 64 publications

References 29 publications

Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints

Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints

A survey on biometric cryptosystems and cancelable biometrics

Embedding edit distance to enable private keyword search

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