LEADER 02991nam 2200541Ia 450 001 9910709935903321 005 20180711120936.0 024 8 $aGOVPUB-C13-e9a97fef7f83d68dea1e984db38fb271 035 $a(CKB)5470000002474909 035 $a(OCoLC)123903905 035 $a(OCoLC)995470000002474909 035 $a(EXLCZ)995470000002474909 100 $a20070501d2005 ua 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSlap fingerprint segmentation evaluation 2004 analysis report /$fBradford Ulery [and others] 210 1$a[Gaithersburg, MD] :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d[2005]. 215 $a1 online resource (8 pages) $cillustrations 225 1 $aNISTIR ;$v7209 300 $a"8 March 2005." 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aTitle from page 1, viewed March 13, 2007. 330 $aThe Slap Fingerprint Segmentation Evaluation 2004 (SlapSeg04) was conducted to assess the accuracy of algorithms used to segment slap fingerprint images into individual fingerprint images. Thirteen slap segmentation applications from ten different organizations were evaluated using data from seven government sources. The source of data, the segmentation software used, and the scoring criteria used were each found to have a significant impact on accuracy. The most accurate segmenters produced at least three highly matchable fingers and correctly identified finger positions in from 93% to over 99% of the slap images, depending on the data source. The data source had a much greater effect on success rate than whether the images were collected using livescan devices or paper. Most segmenters achieved comparable accuracies on the better quality data, but there were significant differences among segmenters when processing poor quality data. Some segmenters are capable of identifying many, but not all, problem slaps: failure rates could be cut substantially by allowing some of the slaps to be recaptured or rejected. 606 $aFingerprints$xIdentification$xAutomation 606 $aImage processing$xTesting 615 0$aFingerprints$xIdentification$xAutomation. 615 0$aImage processing$xTesting. 701 $aHicklin$b R. Austin$01388770 701 $aIndovina$b Michael$01410826 701 $aKwong$b Kayee$01394807 701 $aUlery$b Bradford$01403323 701 $aWatson$b C. I$g(Craig I.)$01387954 712 02$aMitretek Systems. 712 02$aNational Institute of Standards and Technology (U.S.) 801 0$bNBS 801 1$bNBS 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 906 $aBOOK 912 $a9910709935903321 996 $aSlap fingerprint segmentation evaluation 2004 analysis report$93500369 997 $aUNINA