Sinossi
Biometrics provides greater security and usability than conventional identification methods. In order to render an effective biometric system this book provides an improved multi-model biometric recognition based on iris and fingerprints. The work is established on three modules which are pre-processing module, feature extraction module and recognition module. At first, pre-processing is performed through histogram equalization on input image to enhance its quality. Then, the extraction of the feature set is performed which include modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features. Consequently, the optimum function is found with the Rider Optimization Algorithm(ROA). Fusion of optimized fingerprint and iris features is carried out with the help of Fish Swarm optimization algorithm. Further, Improved Multi Kernel Support vector machine (IMKSVM) and Deep Neural Network (DNN) algorithms are used for authentication process. In IMKSVM, several kernels are integrated to give shape to a hybrid kernel. Alternatively, DNN is a multi-layered artificial neural network which finds the right mathematical transformation to turn input into output.
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Multimodal Biometric Identification System
Editore:
Scholars' Press Mai 2023, 2023
ISBN 10: 6205522578
ISBN 13: 9786205522578
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Biometrics provides greater security and usability than conventional identification methods. In order to render an effective biometric system this book provides an improved multi-model biometric recognition based on iris and fingerprints. The work is established on three modules which are pre-processing module, feature extraction module and recognition module. At first, pre-processing is performed through histogram equalization on input image to enhance its quality. Then, the extraction of the feature set is performed which include modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features. Consequently, the optimum function is found with the Rider Optimization Algorithm(ROA). Fusion of optimized fingerprint and iris features is carried out with the help of Fish Swarm optimization algorithm. Further, Improved Multi Kernel Support vector machine (IMKSVM) and Deep Neural Network (DNN) algorithms are used for authentication process. In IMKSVM, several kernels are integrated to give shape to a hybrid kernel. Alternatively, DNN is a multi-layered artificial neural network which finds the right mathematical transformation to turn input into output. 188 pp. Englisch. Codice articolo 9786205522578
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Multimodal Biometric Identification System: Based on Fingerprint & Iris
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahuja RinkyDr. Rinky Ahuja is an Assistant Professor in School of Engineering & Technology at Sushant University.Dr. Latika is a Professor and Associate Dean, School of Engineering & Technology at Sushant University.Dr. Neha Gupta is. Codice articolo 892534697
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Multimodal Biometric Identification System: Based on Fingerprint & Iris
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Multimodal Biometric Identification System
Editore:
Scholars' Press Mai 2023, 2023
ISBN 10: 6205522578
ISBN 13: 9786205522578
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Biometrics provides greater security and usability than conventional identification methods. In order to render an effective biometric system this book provides an improved multi-model biometric recognition based on iris and fingerprints. The work is established on three modules which are pre-processing module, feature extraction module and recognition module. At first, pre-processing is performed through histogram equalization on input image to enhance its quality. Then, the extraction of the feature set is performed which include modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features. Consequently, the optimum function is found with the Rider Optimization Algorithm(ROA). Fusion of optimized fingerprint and iris features is carried out with the help of Fish Swarm optimization algorithm. Further, Improved Multi Kernel Support vector machine (IMKSVM) and Deep Neural Network (DNN) algorithms are used for authentication process. In IMKSVM, several kernels are integrated to give shape to a hybrid kernel. Alternatively, DNN is a multi-layered artificial neural network which finds the right mathematical transformation to turn input into output.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 188 pp. Englisch. Codice articolo 9786205522578
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Multimodal Biometric Identification System : Based on Fingerprint & Iris
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Biometrics provides greater security and usability than conventional identification methods. In order to render an effective biometric system this book provides an improved multi-model biometric recognition based on iris and fingerprints. The work is established on three modules which are pre-processing module, feature extraction module and recognition module. At first, pre-processing is performed through histogram equalization on input image to enhance its quality. Then, the extraction of the feature set is performed which include modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features. Consequently, the optimum function is found with the Rider Optimization Algorithm(ROA). Fusion of optimized fingerprint and iris features is carried out with the help of Fish Swarm optimization algorithm. Further, Improved Multi Kernel Support vector machine (IMKSVM) and Deep Neural Network (DNN) algorithms are used for authentication process. In IMKSVM, several kernels are integrated to give shape to a hybrid kernel. Alternatively, DNN is a multi-layered artificial neural network which finds the right mathematical transformation to turn input into output. Codice articolo 9786205522578
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