LEADER 03647nam 2200637Ia 450 001 9910709940303321 005 20180711120938.0 024 8 $aGOVPUB-C13-367ee6d5c84e406b4b32ec916edf1a81 035 $a(CKB)5470000002474865 035 $a(OCoLC)475568095 035 $a(OCoLC)995470000002474865 035 $a(EXLCZ)995470000002474865 100 $a20091208d2009 ua 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHypothesis test of fingerprint-image matching algorithms in operational ROC analysis /$fJin Chu Wu, Alvin F. Martin, Raghu N. Kacker 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d[2009]. 215 $a1 online resource (ii, 23 pages) $cillustrations 225 1 $aNISTIR ;$v7586 300 $a"June 2009." 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aTitle from page [1], viewed December 8, 2009. 320 $aIncludes bibliographical references (pages 22-23). 330 $aTo evaluate the performance of fingerprint-image matching algorithms on large datasets, a receiver operating characteristic (ROC) curve is applied. From the operational perspective, the true accept rate (TAR) of the genuine scores at a specified false accept rate (FAR) of the impostor scores is usually employed. And the equal error rate (EER) can also be used. The accuracies of the measurement TAR and EER in terms of standard errors and 95 % confidence intervals can be computed using the nonparametric two-sample bootstrap based on our studies of bootstrap variability on large fingerprint datasets. In this article, the hypothesis testing is performed to determine whether the difference between the performance of one algorithm and a hypothesized value, or the difference between the performances of two algorithms where the correlation is taken into account is statistically significant. In the case that the alternative hypothesis is accepted, the sign of the difference is employed to determine which is better than the other. Examples are provided. 517 3 $aHypothesis test of fingerprint-image matching algorithms in operational Receiver Operating Characteristic analysis 606 $aBiometric identification 606 $aFingerprints$xData processing 606 $aImage processing$xDigital techniques 606 $aBiometric identification$2fast 606 $aFingerprints$xData processing$2fast 606 $aImage processing$xDigital techniques$2fast 615 0$aBiometric identification. 615 0$aFingerprints$xData processing. 615 0$aImage processing$xDigital techniques. 615 7$aBiometric identification. 615 7$aFingerprints$xData processing. 615 7$aImage processing$xDigital techniques. 700 $aWu$b Jin Chu$01391473 701 $aKacker$b Raghu$01388742 701 $aMartin$b Alvin F$01394685 712 02$aInformation Technology Laboratory (National Institute of Standards and Technology).$bInformation Access Division. 712 02$aInformation Technology Laboratory (National Institute of Standards and Technology).$bMathematical and Computational Sciences Division. 801 0$bNBS 801 1$bNBS 801 2$bNBS 801 2$bOCLCQ 801 2$bOCLCF 801 2$bOCLCO 801 2$bOCLCQ 906 $aBOOK 912 $a9910709940303321 996 $aHypothesis test of fingerprint-image matching algorithms in operational ROC analysis$93459961 997 $aUNINA