Biometric security and privacy : opportunities & challenges in the big data era

This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as quality issue, biometric template prote...

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Bibliographic Details
Other Authors: Jiang, Richard, (Editor), Al-madeed, Somaya, (Editor), Bouridane, Ahmed, (Editor), Crookes, Danny, 1956- (Editor), Beghdadi, A. (Editor)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2017.
Series: Signal processing for security technologies.
Subjects:
Biometric identification.
Big data.
Artificial intelligence.
elektronické knihy
electronic books
ISBN: 9783319473017
9783319473000
Physical Description: 1 online resource

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Table of contents

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245 0 0 |a Biometric security and privacy :  |b opportunities & challenges in the big data era /  |c Richard Jiang, Somaya Al-maadeed, Ahmed Bouridane, Prof. Danny Crookes, Azeddine Beghdadi, editors. 
260 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Signal processing for security technologies 
505 0 |a Preface; Contents; 1 Fingerprint Quality Assessment: Matching Performance and Image Quality; 1.1 Introduction; 1.2 Background; 1.3 Trial Measures; 1.3.1 Metrics with Single Feature; 1.3.2 Segmentation-Based Metrics; 1.3.2.1 FQA via Informative Region; 1.3.2.2 FQA via Pixel-Pruning; 1.3.3 FQA via Multi-feature; 1.4 Experimental Results; 1.4.1 Software; 1.4.2 Database et Protocol; 1.4.3 Results; 1.4.3.1 ES with Quality; 1.4.3.2 Isometric Bins; 1.4.4 Discussion via Sample Utility; 1.5 Conclusion; References. 
505 8 |a 2 A Novel Perspective on Hand Vein Patterns for Biometric Recognition: Problems, Challenges, and Implementations2.1 Introduction; 2.2 Vein Pattern Scanning Using Optical Methods; 2.2.1 Vein Pattern Visualization; 2.2.2 Structure of a Hand Vein Recognition Device; 2.3 Problems and Challenges in Vein Pattern Applications; 2.4 Modern Perspectives on Vein Structure Recognition; 2.4.1 Ergonomics and Hand Pose Assessment in Vein pattern Identification; 2.4.2 Synthetic Vein Pattern Generation; 2.4.3 Vein Biometrics in a Connected World; 2.5 Conclusions; References. 
505 8 |a 3 Improving Biometric Identification Performance Using PCANet Deep Learning and Multispectral Palmprint3.1 Introduction; 3.2 Image Features; 3.3 Proposed Methodology; 3.3.1 Feature Extraction; 3.3.1.1 PCANet Deep Learning; 3.3.1.2 Flexibility Property; 3.3.2 Classification; 3.4 Experimental Results and Discussion; 3.4.1 Experimental Databases; 3.4.2 Identification Test Results; 3.4.2.1 Performance of the Unimodal Systems; 3.4.2.2 Performance of the Multimodal Systems; 3.4.3 Comparison Study; 3.5 Conclusion and Further Work; References; 4 Biometric Acoustic Ear Recognition; 4.1 Introduction. 
505 8 |a 4.2 Ear Biometrics and Acoustics4.2.1 The Ear as a Biometric; 4.2.2 Image Based Ear Recognition; 4.2.3 Acoustic Based Ear Recognition; 4.2.4 Acoustic Properties of The Ear; 4.2.5 Ears Coupled to Headphones; 4.3 Measuring Device; 4.4 Experiments; 4.4.1 Design Considerations; 4.4.2 Data Collection; 4.5 Data Analysis; 4.5.1 Preprocessing; 4.5.2 Initial Data Analysis; 4.5.3 Statistical Attributes; 4.5.4 Feature Selection and Extraction; 4.5.4.1 All Frequency Components; 4.5.4.2 Octave Bands; 4.5.4.3 Acoustic Properties of The Outer Ear; 4.6 Results; 4.6.1 Performance of All Frequency Components. 
505 8 |a 4.6.2 Performance of PCA4.6.3 Performance of Octave Bands; 4.6.4 Performance of Ear Characteristic Bands and Peaks; 4.7 Discussion and Future Work; 4.8 Conclusion; References; 5 Eye Blinking EOG Signals as Biometrics; 5.1 Introduction; 5.2 Origin of Eye Blinking EOG Signals; 5.3 Proposed Approach for Eye Blinking EOG Biometric System; 5.3.1 Data Acquisition; 5.3.2 Pre-processing; 5.3.3 Feature Extraction; 5.3.4 Feature Selection; 5.3.5 Classification; 5.3.5.1 Linear Decision Rule; 5.3.5.2 Mahalanobis Decision Rule; 5.4 Experimental Setup and Results; 5.4.1 Identification Mode. 
500 |a Includes index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as quality issue, biometric template protection, and anti-spoofing. The book also focuses on several emerging topics such as big data issues, mobile biometrics and multispectral biometrics, and includes a number of new biometrics such as vein pattern, acoustic biometrics, eye-blinking EOG, ECG, gait and handwriting. Authors also show how to use biometrics in cyber security applications and its relevant legal matters under EU legislation. The contributors cover the topics, their methods, and their applications in depth. 
504 |a Includes bibliographical references at the end of each chapters and index. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Biometric identification. 
650 0 |a Big data. 
650 0 |a Artificial intelligence. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Jiang, Richard,  |e editor. 
700 1 |a Al-madeed, Somaya,  |e editor. 
700 1 |a Bouridane, Ahmed,  |e editor. 
700 1 |a Crookes, Danny,  |d 1956-  |e editor. 
700 1 |a Beghdadi, A.  |q (Azeddine),  |e editor. 
776 0 8 |i Print version:  |z 9783319473000  |z 331947300X  |w (OCoLC)958460434 
830 0 |a Signal processing for security technologies. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-47301-7  |y Plný text 
992 |c NTK-SpringerENG 
999 |c 99658  |d 99658 
993 |x NEPOSILAT  |y EIZ 

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