On estimating performance indices for biometric identification

Bhatnagar, Jay R.; Kumar, Ajay

doi:10.1016/j.patcog.2008.10.004

Cited by 21 publications

(26 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now, assuming that such feature subsets are independent then the PRC for 210 query minutiae points, 210 reference minutiae points can be given as ( ) . We point out that the acquisition and processing of fingerprint and palmprint biometrics is quite similar [11].…”

Section: Prc Of Fingerprint Biometricsmentioning

confidence: 96%

“…This is combined with s N , F whose values are taken from relevant literature to compute b R using Equation (10). Finally, the error exponent is computed using Equation (11) which gives the value of PRC. Our goal is to consider a user population, denoted as K, which is large enough so that the noisy channel with its input as K templates can be regarded as stationary.…”

Section: Simulation Illustrating Use Of Error Exponent To Estimate Prmentioning

confidence: 99%

“…This variability in rate is reflected in the error exponent using Equation (11) and finally the PRC is obtained using Equation (12).…”

Section: Simulation Illustrating Use Of Error Exponent To Estimate Prmentioning

confidence: 99%

See 2 more Smart Citations

On formulating a probability of random correspondence of biometrics using error exponents

Bhatnagar

Lall

Patney

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

View full text Add to dashboard Cite

Performance of biometrics in human authentication can be limited when multiple samples of a user's biometric information differ due to intra-class variability in acquisition, storage or transmission of biometrics. Thus, random correspondence results between users. We formulate Probability of Random Correspondence (PRC) by developing an information model of biometrics features as a noisy source. The information in features represented by N s bits inherently has t error bits attributed to the intra-class variabilities. The values of t and bit error probability are shown to be determined from second order statistics of the features. These are used respectively, to formulate information rate of the noisy biometric and to characterize a binary symmetric channel that models the occurrence of errors in a biometric template. Finally, information rate and error probability are combined in the framework of error exponents to formulate PRC of biometrics. We illustrate our approach with simulations, using freely available data, to obtain numerical values of PRC of fingerprint biometrics.

show abstract

Section: Prc Of Fingerprint Biometricsmentioning

confidence: 96%

Section: Simulation Illustrating Use Of Error Exponent To Estimate Prmentioning

confidence: 99%

See 1 more Smart Citation

On formulating a probability of random correspondence of biometrics using error exponents

Bhatnagar

Lall

Patney

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

View full text Add to dashboard Cite

show abstract

“…Looking for the solution of this problem without degrading the system's performance brings the researcher's attentions toward the proper selection of cohort subset. Researchers are also working on multimodal biometric systems [10][11][12] which outperform mono-modal biometric systems. However, with the increase of number of modalities and number of subjects in database, computational cost is proportionally increased.…”

Section: Introductionmentioning

confidence: 99%

Cohort Selection of Specific User Using Max-Min-Centroid-Cluster (MMCC) Method to Enhance the Performance of a Biometric System

Garain¹,

Kumar²,

Sanyal³

et al. 2015

IJSIA

View full text Add to dashboard Cite

show abstract

“…2. Two feature vectors are matched using feature matching or similarity measurement method and the matching score generated is compared with reference threshold value [6] [9]. It is basically the value of reference threshold that authenticates the person as genuine or imposter.…”

Section: Introductionmentioning

confidence: 99%

Reference Threshold Calculation for Biometric Authentication

Malik¹,

Girdhar²,

Dahiya³

et al. 2014

IJIGSP

View full text Add to dashboard Cite

-In biometric systems, reference threshold is defined as a value that can decide the authenticity of a person. Authenticity means whether the person is genuine or intruder. The statistical calculation of various values like reference threshold, FAR (False Acceptance Rate), FRR (False Rejection Rate) are required for realtime automated biometric authentication system because the measurement of biometric features are statistical values. In this paper, the need of reference threshold, how reference threshold value is calculated is explained mathematically. Various factors on which reference threshold value depends are discussed. It is also explained that how selection of correct value of reference threshold plays an important role in authentication system. Experimental results describe the selection of reference threshold value for palmprint biometric system.

show abstract

On estimating performance indices for biometric identification

Abstract: Abstract

Cited by 21 publications

References 26 publications

On formulating a probability of random correspondence of biometrics using error exponents

On formulating a probability of random correspondence of biometrics using error exponents

Cohort Selection of Specific User Using Max-Min-Centroid-Cluster (MMCC) Method to Enhance the Performance of a Biometric System

Reference Threshold Calculation for Biometric Authentication

Contact Info

Product

Resources

About