Filterbank-based fingerprint matching

Jain, Anil K.; Prabhakar, Salil; Lin, Hong; Pankanti, Sharath

doi:10.1109/83.841531

Cited by 947 publications

(490 citation statements)

References 26 publications

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“…First, it is very di cult to extract complete ridge structures automatically for a considerable fraction of the population, which obviously prevents ÿngerprint recognition from getting a generalized commercial use. Besides, it is a di cult problem to match two ÿn-gerprint representations when they contain a di erent number of minutiae points [37]. These shortcomings make it a desirable goal to develop new strategies that allow ÿngerprint recognition gain a much wider use.…”

Section: Fingerprint Identiÿcationmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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In this paper, we provide an overview of the fundamentals of biometric identiÿcation, together with a description of the main biometric technologies currently in use, all of them within a common reference framework. A comparison on di erent qualitative parameters of these technologies is also given, so that the reader may have a clear perspective of advantages and disadvantages of each. A section on multibiometrics describes the state of the art in making these systems work coordinately. Fusion at di erent conceptual levels is described. Finally, a section on commercial issues provides the reader a perspective of the main companies currently involved in this ÿeld. ?

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Section: Fingerprint Identiÿcationmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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“…3(a). To obtain discriminative local features, an average absolute deviation (AAD) in  ×  blocks of each filtered image is constructed [2] [21] .…”

Section: ⅲ Gabor Feature Extractionmentioning

confidence: 99%

Finger Vein Recognition based on Matching Score-Level Fusion of Gabor Features

Yu¹,

Yoon²,

Park³

2013

The Journal of Korean Institute of Communications and Informati

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Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.

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“…Precisely, FingerCode approach proposed by Jain eta al in [16] has been selected due to the fact that Random Orthonormal Projection (ROP) [7,8] requires fixed length feature vectors. FingerCode approach [16] relies on detecting the Region Of Interest (ROI) and tessellating it around the reference point, then a bank of Gabor filters are applied in eight directions to capture both local and global features of a fingerprint image as illustrated by Figure 1.…”

Section: Biometric Authentication and Key Generationmentioning

confidence: 99%

Privacy preserving, real-time and location secured biometrics for mCommerce authentication

et al. 2011

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Secure wireless connectivity between mobile devices and financial/commercial establishments is mature, and so is the security of remote authentication for mCommerce. However, the current techniques are open for hacking, false misrepresentation, replay and other attacks. This is because of the lack of real-time and current-precise-location in the authentication process. This paper proposes a new technique that includes freshly-generated real-time personal biometric data of the client and present-position of the mobile device used by the client to perform the mCommerce so to form a real-time biometric representation to authenticate any remote transaction. A fresh GPS fix generates the "time and location" to stamp the biometric data freshly captured to produce a single, real-time biometric representation on the mobile device. A trusted Certification Authority (CA) acts as an independent authenticator of such client's claimed realtime location and his/her provided fresh biometric data. Thus eliminates the necessity of user enrolment with many mCommerce services and application providers. This CA can also "independently from the client" and "at that instant of time" collect the client's mobile device "time and location" from the cellular network operator so to compare with the received information, together with the client's stored biometric information. Finally, to preserve the client's location privacy and to eliminate the possibility of cross-application client tracking, this paper proposes shielding the real location of the mobile device used prior to submission to the CA or authenticators.

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Filterbank-based fingerprint matching

Cited by 947 publications

References 26 publications

Biometric identification systems

Biometric identification systems

Finger Vein Recognition based on Matching Score-Level Fusion of Gabor Features

Privacy preserving, real-time and location secured biometrics for mCommerce authentication

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