Multibiometrics for human identification /
Material type: TextPublication details: Cambridge ; New York : Cambridge University Press, 2011Description: xiv, 388 p., [4] p. of plates : ill. (some col.)ISBN: 9780521115964 (hardback); 0521115965 (hardback)Subject(s): Biometric identificationDDC classification: 006.4 Summary: "In today's security-conscious society, real-world applications for authentication or identification require a highly accurate system for recognizing individual humans. The required level of performance cannot be achieved through the use of a single biometric such as face, fingerprint, ear, iris, palm, gait, or speech. Fusing multiple biometrics enables the indexing of large databases, more robust performance, and enhanced coverage of populations. Multiple biometrics are also naturally more robust against attacks than single biometrics. This book addresses a broad spectrum of research issues on multibiometrics for human identification, ranging from sensing modes and modalities to fusion of biometric samples and combination of algorithms. It covers publicly available multibiometrics databases, theoretical and empirical studies on sensor fusion techniques in the context of biometrics authentication, identification, and performance evaluation and prediction"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Kannur University Central Library Stack | Stack | 006.4 MUL (Browse shelf (Opens below)) | Available | 31337 |
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006.37 PRI/C Computer vision :Models, learning, and inference | 006.37 UMB/D Digital image processing and analysis : | 006.3843 LIP/I Introduction to quantum algorithms via linear algebra | 006.4 MUL Multibiometrics for human identification / | 006.4 RIP/P Pattern recognition and neural networks / | 006.4 SAN/P Pattern recognition : from classical to modern approaches | 006.4 WEB/S Statistical pattern recognition / |
"In today's security-conscious society, real-world applications for authentication or identification require a highly accurate system for recognizing individual humans. The required level of performance cannot be achieved through the use of a single biometric such as face, fingerprint, ear, iris, palm, gait, or speech. Fusing multiple biometrics enables the indexing of large databases, more robust performance, and enhanced coverage of populations. Multiple biometrics are also naturally more robust against attacks than single biometrics. This book addresses a broad spectrum of research issues on multibiometrics for human identification, ranging from sensing modes and modalities to fusion of biometric samples and combination of algorithms. It covers publicly available multibiometrics databases, theoretical and empirical studies on sensor fusion techniques in the context of biometrics authentication, identification, and performance evaluation and prediction"--
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