LEADER 06512nam 22008535 450 001 996466219603316 005 20200704151332.0 010 $a3-540-47917-1 024 7 $a10.1007/3-540-47917-1 035 $a(CKB)1000000000211760 035 $a(SSID)ssj0000316511 035 $a(PQKBManifestationID)11224859 035 $a(PQKBTitleCode)TC0000316511 035 $a(PQKBWorkID)10276120 035 $a(PQKB)11574192 035 $a(DE-He213)978-3-540-47917-8 035 $a(MiAaPQ)EBC3062513 035 $a(MiAaPQ)EBC6283588 035 $a(PPN)123720435 035 $a(EXLCZ)991000000000211760 100 $a20100301d2002 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aBiometric Authentication$b[electronic resource] $eInternational ECCV 2002 Workshop Copenhagen, Denmark, June 1, 2002 Proceedings /$fedited by Massimo Tistarelli, Josef Bigun, Anil K. Jain 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (X, 202 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v2359 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-43723-1 320 $aIncludes bibliographical references and index. 327 $aFace Recognition I -- An Incremental Learning Algorithm for Face Recognition -- Face Recognition Based on ICA Combined with FLD -- Understanding Iconic Image-Based Face Biometrics -- Fusion of LDA and PCA for Face Verification -- Fingerprint Recognition -- Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment -- Fingerprint Matching Using Feature Space Correlation -- Fingerprint Minutiae: A Constructive Definition -- Psychology and Biometrics -- Pseudo-entropy Similarity for Human Biometrics -- Mental Characteristics of Person as Basic Biometrics -- Face Detection and Localization -- Detection of Frontal Faces in Video Streams -- Genetic Model Optimization for Hausdorff Distance-Based Face Localization -- Coarse to Fine Face Detection Based on Skin Color Adaption -- Face Recognition II -- Robust Face Recognition Using Dynamic Space Warping -- Subspace Classification for Face Recognition -- Gait and Signature Analysis -- Gait Appearance for Recognition -- View-invariant Estimation of Height and Stride for Gait Recognition -- Improvement of On-line Signature Verification System Robust to Intersession Variability -- Classifiers for Recognition -- Biometric Identification in Forensic Cases According to the Bayesian Approach -- A New Quadratic Classifier Applied to Biometric Recognition. 330 $aBiometric authentication refers to identifying an individual based on his or her distinguishing physiological and/or behavioral characteristics. It associates an individual with a previously determined identity based on that individual s appearance or behavior. Because many physiological or behavioral characteristics (biometric indicators) are distinctive to each person, biometric identifiers are inherently more reliable and more capable than knowledge-based (e.g., password) and token-based (e.g., a key) techniques in differentiating between an authorized person and a fraudulent impostor. For this reason, more and more organizations are looking to automated identity authentication systems to improve customer satisfaction, security, and operating efficiency as well as to save critical resources. Biometric authentication is a challenging pattern recognition problem; it involves more than just template matching. The intrinsic nature of biometric data must be carefully studied, analyzed, and its properties taken into account in developing suitable representation and matching algorithms. The intrinsic variability of data with time and environmental conditions, the social acceptability and invasiveness of acquisition devices, and the facility with which the data can be counterfeited must be considered in the choice of a biometric indicator for a given application. In order to deploy a biometric authentication system, one must consider its reliability, accuracy, applicability, and efficiency. Eventually, it may be necessary to combine several biometric indicators (multimodal-biometrics) to cope with the drawbacks of the individual biometric indicators. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v2359 606 $aOptical data processing 606 $aPattern recognition 606 $aNatural language processing (Computer science) 606 $aComputers and civilization 606 $aManagement information systems 606 $aComputer science 606 $aBioinformatics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aComputers and Society$3https://scigraph.springernature.com/ontologies/product-market-codes/I24040 606 $aManagement of Computing and Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I24067 606 $aBioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15001 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aNatural language processing (Computer science). 615 0$aComputers and civilization. 615 0$aManagement information systems. 615 0$aComputer science. 615 0$aBioinformatics. 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aNatural Language Processing (NLP). 615 24$aComputers and Society. 615 24$aManagement of Computing and Information Systems. 615 24$aBioinformatics. 676 $a005 702 $aTistarelli$b Massimo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBigun$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJain$b Anil K$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466219603316 996 $aBiometric Authentication$9772543 997 $aUNISA