MARC record from Internet Archive

LEADER: 09819cam 2200361 i 4500
001 9925304402301661
005 20170607051607.9
008 160407s2017 flua b 001 0 eng
010 $a 2016016651
019 $a947020390$a947794812
020 $a9781498784627$qhardcover
020 $a1498784623$qhardcover
035 $a99975450467
035 $a(OCoLC)946610717$z(OCoLC)947020390$z(OCoLC)947794812
035 $a(OCoLC)ocn946610717
040 $aDLC$beng$erda$cDLC$dOCLCF$dBTCTA$dOCLCO$dYDXCP$dBDX$dYDX$dOCLCO$dH9Z$dIPL
042 $apcc
050 00 $aTK7882.B56$bS236 2017
082 00 $a006.2/48$223
100 1 $aSaeed, Khalid$c(Computer scientist),$eauthor.
245 10 $aNew directions in behavioral biometrics /$cKhalid Saeed ; with Marcin Adamski, Tapalina Bhattasali, Mohammad K. Nammous, Piotr Panasiuk, Mariusz Rybnik, and Soharab H. Shaikh.
264 1 $aBoca Raton, FL :$bCRC Press, Taylor & Francis Group,$c[2017]
300 $axix, 228 pages ;$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
504 $aIncludes bibliographical references and index.
505 00 $aMachine generated contents note:$gch. 1$tIntroduction to Behavioral Biometrics --$g1.1.$tBehaviometrics --$g1.1.1.$tHow It Works --$g1.1.2.$tMajor Benefits --$g1.2.$tWhat Is Special about Behavioral Biometrics Data Acquisition? --$g1.3.$tBehavioral Biometrics Features --$g1.4.$tClassification of Behavioral Biometrics Traits --$g1.5.$tProperties of Few Behavioral Biometrics --$g1.5.1.$tSignature --$g1.5.1.1.$tConstraints of Signature Recognition --$g1.5.1.2.$tMerits of Signature Recognition --$g1.5.1.3.$tDemerits of Signature Recognition --$g1.5.1.4.$tApplications of Signature Recognition --$g1.5.2.$tKeystroke Dynamics --$g1.5.2.1.$tMerits of Keystroke Recognition --$g1.5.2.2.$tDemerits of Keystroke Recognition --$g1.5.2.3.$tApplication of Keystroke Recognition --$g1.5.3.$tGait --$g1.5.3.1.$tMerits of Gait Recognition --$g1.5.3.2.$tDemerits of Gait Recognition --$g1.5.3.3.$tApplication of Gait Recognition --$g1.5.4.$tVoice --$g1.5.4.1.$tDifferences between Voice and Speech Recognition --$g1.5.4.2.$tMerits of Voice Recognition --$g1.5.4.3.$tDemerits of Voice Recognition --$g1.5.4.4.$tApplications of Voice Recognition --$g1.6.$tBehavioral Biometrics Data Acquisition Device --$g1.7.$tBehavioral Biometrics Recognition Systems --$g1.7.1.$tAccomplishment of Behavioral Biometrics Systems --$g1.7.2.$tInitial Processing and Analysis of Biometric Traits --$g1.7.3.$tFramework --$g1.8.$tGeneralized Algorithm --$g1.9.$tPerformance Measurement --$g1.9.1.$tBenchmark Definition --$g1.9.2.$tRobustness Analysis --$g1.9.3.$tDiscussion --$g1.10.$tEvaluation of Behavioral Biometric Systems --$g1.10.1.$tDiscussion --$g1.11.$tComparison and Analysis --$g1.12.$tHuman Measurement and Evaluation on the Basis of Behavioral Biometric Features --$g1.12.1.$tVerification and Identification --$g1.12.2.$tError Sources in Behavioral Biometrics --$g1.13.$tTypes of Basic Verification Errors and Their Rates --$g1.13.1.$tError Graphical Representation --$g1.13.2.$tFurther Study of Errors --$g1.14.$tOpen Issues --$g1.14.1.$tCollection of Sensitive Information --$g1.14.2.$tNegative Reaction to Obtrusive Equipment --$g1.14.3.$tConsent and Secondary Use for Data Collected with Unobtrusive Equipment --$g1.14.4.$tSensitivity to Change of Application Configuration --$g1.14.5.$tSpoofing Attacks --$g1.15.$tFuture Trends --$g1.16.$tApplication Area --$g1.17.$tBehavioral Biometrics Used in Real-Time Application --$g1.18.$tConclusions --$tReferences --$gch. 2$tSignature Recognition --$g2.1.$tBrief History of Handwriting Analysis --$g2.2.$tAutomated Systems for Signature Recognition --$g2.3.$tOffline and Online Signatures --$g2.4.$tTypes of Forgeries --$g2.5.$tDatabases for Signature System Evaluation --$g2.5.1.$tSVC2004 --$g2.5.2.$tGPDS-960 --$g2.5.3.$tMCYT-100 --$g2.5.4.$tBIOMET --$g2.6.$tCommercial Software --$g2.6.1.$tSOFTPRO --$g2.6.2.$tParaScript --$g2.6.3.$tSQN Banking Systems --$g2.7.$tA Review to Signature Recognizers --$g2.7.1.$tData Acquisition --$g2.7.2.$tPreprocessing --$g2.7.3.$tFeature Extraction --$g2.7.3.1.$tGraphology Based --$g2.7.3.2.$tShape Context Based --$g2.7.3.3.$tContour Based --$g2.7.3.4.$tProjection Based --$g2.7.3.5.$tCurvature Based --$g2.7.3.6.$tRadon Transform Based --$g2.7.3.7.$tHough Transform Based --$g2.7.3.8.$tTexture Based --$g2.7.3.9.$tWavelet Transform Based --$g2.7.4.$tClassification --$g2.7.4.1.$tTemplate Matching --$g2.7.4.2.$tStatistical Classification --$g2.8.$tAssessment of Biometric Signature Systems --$g2.9.$tExample Studies on Signature Recognition --$g2.9.1.$tOnline System --$g2.9.1.1.$tResults --$g2.9.1.2.$tIdentification --$g2.9.1.3.$tVerification --$g2.9.1.4.$tDiscussion --$g2.9.2.$tOffline System --$g2.9.2.1.$tResults --$g2.9.2.2.$tIdentification --$g2.9.2.3.$tVerification --$g2.9.2.4.$tDiscussion --$tReferences --$gch. 3$tKeystroke Dynamics --$g3.1.$tHistory of Keystroke Dynamics --$g3.2.$tKeystroke Analysis --$g3.2.1.$tData Acquisition --$g3.3.$tVariability of Users, User Behavior, and Hardware --$g3.4.$tAuthentication and Identification --$g3.4.1.$tOn Biometrics Context of Keystroke Dynamics --$g3.5.$tCharacteristics of Keystroke Dynamics --$g3.5.1.$tUniversality --$g3.5.2.$tUniqueness --$g3.5.3.$tPermanence --$g3.5.4.$tCollectability --$g3.5.5.$tPerformance --$g3.5.6.$tAcceptability --$g3.5.7.$tCircumvention --$g3.5.8.$tSummary --$g3.6.$tApproaches to Keystroke Dynamics --$g3.6.1.$tTaxonomies of Approaches --$g3.6.2.$tInput Text Approach Taxonomy --$g3.6.3.$tSimple Typing Features --$g3.7.$tAdvanced Approaches --$g3.8.$tFixed Text for All Users --$g3.8.1.$tDataset --$g3.8.2.$tProposed Algorithm --$g3.9.$tFixed Text for Each User (BioPassword/AdmitOneSecurity) --$g3.9.1.$tComputer-Access Security Systems Using Keystroke Dynamics --$g3.9.2.$tAdmitOneSecurity --$g3.10.$tNon-Fixed Text with Regard to Key --$g3.10.1.$tProposed Algorithm --$g3.10.2.$tExperimental Results and Discussion --$g3.11.$tNon-Fixed Text with No Regard to Key --$g3.11.1.$tDataset --$g3.11.2.$tProposed Algorithm --$g3.12.$tContinuous Authentication --$g3.13.$tPerspectives --$g3.14.$tModern Trends and Commercial Applications for Keystroke Dynamics --$g3.14.1.$tErrors Made by Users and Their Correction Methods --$g3.14.2.$tPressure-Sensitive Keyboards --$g3.14.3.$tMobile Phone Keyboards --$g3.14.4.$tATM Hardware --$g3.14.5.$tRandom Numbers Generation --$g3.14.6.$tTiming Attacks on Secure Communications --$g3.14.7.$tExamples of Commercial Applications --$g3.15.$tLegal Issues --$g3.16.$tConclusions --$tReferences --$gch. 4$tGait Analysis --$g4.1.$tHuman Gait Recognition --$g4.2.$tFeatures of Gait Analysis --$g4.3.$tApplications of Gait Analysis --$g4.4.$tGait Cycle --$g4.5.$tDescribing a Stance --$g4.6.$tWhy Does Gait Change from Person to Person or from Time to Time? --$g4.7.$tA Brief Review of the Literature on Human Gait Recognition --$g4.8.$tResearch Challenges --$g4.8.1.$tExternal Factors --$g4.8.2.$tInternal Factors --$g4.9.$tGait Databases for Research --$g4.9.1.$tCASIA-A --$g4.9.2.$tCASIA-B --$g4.9.3.$tCMU MoBo --$g4.9.4.$tUSF Dataset --$g4.9.5.$tSouthampton Dataset --$g4.9.6.$t3D Dataset --$g4.9.7.$tUMD Dataset --$g4.9.8.$tTUM-IITKGP Dataset --$g4.9.9.$tOU-ISIR Database --$g4.10.$tGait Recognition Using Partial Silhouette-Based Approach --$g4.10.1.$tMotivation of the Partial Silhouette-Based Approach --$g4.10.2.$tDynamic Features of Gait -- Why Partial Silhouette? --$g4.10.3.$tPartial Silhouette-Based Methodology --$g4.10.4.$tPreprocessing for Removing Noise --$g4.10.5.$tGait Cycle Detection and Extraction of Landmark Frames --$g4.11.$tExtraction of Partial Silhouette --$g4.11.1.$tBounding Box --$g4.11.2.$tImage Segmentation --$g4.11.3.$tFeature Extraction --$g4.11.4.$tClassification --$g4.11.5.$tTraining --$g4.11.6.$tTesting --$g4.12.$tExperimental Verification --$g4.12.1.$tResults of Full versus Partial Silhouettes --$g4.13.$tComparison with Other Methods --$g4.14.$tEffectiveness of Partial Silhouette Method in the Presence of Noise --$g4.15.$tTime Complexity of the Partial Silhouette-Based Method --$g4.16.$tConclusions --$tReferences --$gch.
505 00 $t5$tVoice Recognition --$g5.1.$tVoice Recognition --$g5.1.1.$tAdvantages of Voice Recognition over Other Biometric Traits --$g5.1.2.$tMain Steps in Voice Recognition Systems --$g5.2.$tSignal Acquisition and Preprocessing --$g5.2.1.$tBiological Background --$g5.2.2.$tPreprocessing Stage --$g5.2.3.$tFeature Extraction --$g5.3.$tToeplitz Matrix Minimal Eigenvalues Algorithm -- A Survey --$g5.3.1.$tLinear Predictive Coding and Burg's Model --$g5.3.2.$tMel Frequency Cepstral Coefficients --$g5.4.$tClassification Using NNs --$g5.4.1.$tProbabilistic NNs --$g5.4.2.$tRadial Basis Function NNs --$g5.5.$tAchievements in Similar Works --$g5.6.$tAchievements in Voice Recognition --$g5.6.1.$tThe Simplest Case, Uttered Words Recognition --$g5.6.1.1.$tInput Samples and Preprocessing Stage --$g5.6.1.2.$tExperiments and Result --$g5.6.2.$tVoiceprint and Security Systems --$g5.6.2.1.$tPerformance of the Speaker Identification Security System --$g5.6.2.2.$tMultilevel Security for the Spoken Words and Speaker --$g5.6.3.$tText-Independent Speaker Identification --$g5.6.3.1.$tDatabase and Preprocessing --$g5.6.3.2.$tFirst Attempt --$g5.6.3.3.$tAnother Attempt --$g5.6.4.$tWhat about Speaker Verification? --$g5.6.4.1.$tIdentification Treatment --$g5.6.4.2.$tVerify the Speaker -- Claiming It Correctly --$g5.6.4.3.$tTrue Rejection and False Acceptance --$g5.6.4.4.$tExtra Testing Data for Verification --$g5.7.$tConclusions --$tReferences.
650 0 $aBiometric identification.
650 0 $aBehaviorism (Psychology)
947 $hCIRCSTACKS$r31786103109739
980 $a99975450467