Multibiometric Watermarking with Compressive Sensing Theory [electronic resource] : Techniques and Applications /
By: Thanki, Rohit M [author.].
Contributor(s): Dwivedi, Vedvyas J [author.] | Borisagar, Komal R [author.] | SpringerLink (Online service).
Series: Signals and Communication Technology: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XXI, 172 p. 104 illus., 48 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319731834.Subject(s): EXTC Engineering
| Signal, Image and Speech Processing | Computational Linguistics | Natural Language Processing (NLP) | Database ManagementDDC classification: 621.382 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: This book presents multibiometric watermarking techniques for security of biometric data. This book also covers transform domain multibiometric watermarking techniques and their advantages and limitations. The authors have developed novel watermarking techniques with a combination of Compressive Sensing (CS) theory for the security of biometric data at the system database of the biometric system. The authors show how these techniques offer higher robustness, authenticity, better imperceptibility, increased payload capacity, and secure biometric watermarks. They show how to use the CS theory for the security of biometric watermarks before embedding into the host biometric data. The suggested methods may find potential applications in the security of biometric data at various banking applications, access control of laboratories, nuclear power stations, military base, and airports.
This book presents multibiometric watermarking techniques for security of biometric data. This book also covers transform domain multibiometric watermarking techniques and their advantages and limitations. The authors have developed novel watermarking techniques with a combination of Compressive Sensing (CS) theory for the security of biometric data at the system database of the biometric system. The authors show how these techniques offer higher robustness, authenticity, better imperceptibility, increased payload capacity, and secure biometric watermarks. They show how to use the CS theory for the security of biometric watermarks before embedding into the host biometric data. The suggested methods may find potential applications in the security of biometric data at various banking applications, access control of laboratories, nuclear power stations, military base, and airports.
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