LEADER 04482nam 22006735 450 001 9910254199603321 005 20200705071645.0 010 $a3-319-22485-9 024 7 $a10.1007/978-3-319-22485-5 035 $a(CKB)3710000000476880 035 $a(EBL)4178490 035 $a(SSID)ssj0001584689 035 $a(PQKBManifestationID)16265127 035 $a(PQKBTitleCode)TC0001584689 035 $a(PQKBWorkID)14866172 035 $a(PQKB)11334964 035 $a(DE-He213)978-3-319-22485-5 035 $a(MiAaPQ)EBC4178490 035 $a(PPN)190520124 035 $a(EXLCZ)993710000000476880 100 $a20150914d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultispectral Biometrics $eSystems and Applications /$fby David Zhang, Zhenhua Guo, Yazhuo Gong 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (232 p.) 300 $aDescription based upon print version of record. 311 $a3-319-22484-0 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aMultimodal Fusion for Robust Identity Authentication: Role of Liveness Checks -- Multimodal Biometric Person Recognition System Based on Multi-Spectral Palmprint Features Using Fusion of Wavelet Representations -- Audio-Visual Biometrics and Forgery -- Face and ECG Based Multi-Modal Biometric Authentication -- Biometrical Fusion ? Input Statistical Distribution -- Normalization of Infrared Facial Images under Variant Ambient Temperatures -- Use of Spectral Biometrics for Aliveness Detection -- A Contactless Biometric System Using Palm Print and Palm Vein Features -- Liveness Detection in Biometrics -- Fingerprint Recognition -- A Gender Detection Approach -- Improving Iris Recognition Performance Using Quality Measures -- Application of LCS Algorithm to Authenticate Users within Their Mobile Phone Through In-Air Signatures -- Performance Comparison of Principal Component Analysis-Based Face Recognition in Color Space -- Block Coding Schemes Designed for Biometric Authentication -- Perceived Age Estimation from Face Images -- Cell Biometrics Based on Bio-Impedance Measurements -- Hand Biometrics in Mobile Dices. 330 $aDescribing several new biometric technologies, such as high-resolution fingerprint, finger-knuckle-print, multi-spectral backhand, 3D fingerprint, tongueprint, 3D ear, and multi-spectral iris recognition technologies, this book analyzes a number of efficient feature extraction, matching and fusion algorithms and how potential systems have been developed. Focusing on how to develop new biometric technologies based on the requirements of applications, and how to design efficient algorithms to deliver better performance, the work is based on the author?s research with experimental results under different challenging conditions described in the text. The book offers a valuable resource for researchers, professionals and postgraduate students working in the fields of computer vision, pattern recognition, biometrics, and security applications, amongst others. 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aBiometrics (Biology) 606 $aBiomedical engineering 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aBiometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22040 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aBiometrics (Biology). 615 0$aBiomedical engineering. 615 14$aSignal, Image and Speech Processing. 615 24$aBiometrics. 615 24$aBiomedical Engineering and Bioengineering. 676 $a620 700 $aZhang$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763056 702 $aGuo$b Zhenhua$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGong$b Yazhuo$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910254199603321 996 $aMultispectral Biometrics$92505231 997 $aUNINA