Multibiometric Watermarking with Compressive Sensing Theory〈1st ed. 2018〉 : Techniques and Applications

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Multibiometric Watermarking with Compressive Sensing Theory〈1st ed. 2018〉 : Techniques and Applications

  • 著者名:Thanki, Rohit M./Dwivedi, Vedvyas J./Borisagar, Komal R.
  • 価格 ¥10,117 (本体¥9,198)
  • Springer(2018/01/02発売)
  • 春うらら!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/15)
  • ポイント 2,730pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9783319731827
  • eISBN:9783319731834

ファイル: /

Description

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.

Table of Contents

Chapter 1. Introduction.- Chapter 2.Related Works and Background Information.- Chapter 3.Issues in Biometric System and Proposed Research Methodology.- Chapter 4.Multibiometric Watermarking Technique using Discrete Wavelet Trans-form (DWT).- Chapter 5. Multibiometric Watermarking Technique using Discrete Cosine Trans-form (DCT) and Discrete Wavelet Transform (DWT).- Chapter 6.Multibiometric Watermarking Technique using Discrete Wavelet Trans-form (DWT) and Singular Value Decomposition (SVD).- Chapter 7.Multibiometric Watermarking Technique using Fast Discrete Curvelet Transform (FDCuT) and Discrete Cosine Transform (DCT).- Chapter 8.Conclusions and Future Work.

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