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Search Publications by: Jin Chu Wu (Assoc)

Displaying 26 - 32 of 32

Studies of Operational Measurement of ROC Curve on Large Fingerprint Data Sets Using Two-Sample Bootstrap

September 4, 2007

Author(s)

Jin Chu Wu

From the operational perspective, for large-size fingerprint data, a receiver operating characteristic (ROC) curve is usually measured by the true accept rate (TAR) of the genuine scores given a specified false accept rate (FAR) of the impostor scores. The

Nonparametric Analysis of Fingerprint Data on Large Data Sets

December 7, 2006

Author(s)

Jin Chu Wu, Charles Wilson

By executing different fingerprint-image matching algorithms on large data sets, it reveals that the match and non-match similarity scores have no specific underlying distribution function. Thus, it requires a nonparametric analysis for fingerprint-image

Nonparametric Statistical Data Analysis of Fingerprint Minutiae Exchange with Two-Finger Fusion

December 7, 2006

Author(s)

Jin Chu Wu, Michael D. Garris

A nonparametric inferential statistical data analysis is presented. The utility of this method is demonstrated through analyzing results from minutiae exchange with two-finger fusion. The analysis focused on high-accuracy vendors and two modes of matching

An Empirical Study of Sample Size in ROC-Curve Analysis of Fingerprint Data

December 4, 2006

Author(s)

Jin Chu Wu, Charles Wilson

The fingerprint datasets in many cases may exceed millions of samples. Thus, the needed size of a biometric evaluation test sample is an important issue in terms of both accuracy and efficiency. In this article, an empirical study, namely, using Chebyshev

Nonparametric Statistical Data Analysis of Fingerprint Minutiae Exchange with Two-Finger Fusion

December 1, 2006

Author(s)

Jin Chu Wu, Michael D. Garris

A nonparametric inferential statistical data analysis is presented. The utility of this method is demonstrated through analyzing results from minutiae exchange with two-finger fusion. High-accuracy vendors are selected and mean error rates are compared

Using Chebyshev's Inequality to Determine Sample Size in Biometric Evaluation of Fingerprint Data

November 1, 2005

Author(s)

Jin Chu Wu, Charles L. Wilson

For fingerprint dataset, which in many cases may exceed a million samples, the underlying distribution function with respect to similarity scores is unknown. The size of biometric evaluation test sample in these cases is an important question. In this

Nonparametric Analysis of Fingerprint Data

May 2, 2005

Author(s)

Jin Chu Wu, Charles L. Wilson

This paper demonstrates that, for large-scale tests, the match and non-match similarity scores have no specific underlying distribution function. The forms of these distribution functions require a nonparametric approach for the analysis of the fingerprint