Mobile biometrics
This book is about the use of biometrics on mobile/smart phones. An integrated and informative analysis, this is a timely survey of the state of the art research and developments in this rapidly growing area.
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| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
London :
Institution of Engineering and Technology,
2017.
|
| Series: | IET security series ;
3. IET book series on advances in biometrics. |
| Subjects: | |
| ISBN: | 9781785610967 1785610961 9781523112883 1523112883 9781785610950 1785610953 |
| Physical Description: | 1 online resource : illustrations |
Cover
Table of contents
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| 003 | OCoLC | ||
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| 020 | |a 9781785610967 |q (electronic bk.) | ||
| 020 | |a 1785610961 |q (electronic bk.) | ||
| 020 | |a 9781523112883 |q (electronic bk.) | ||
| 020 | |a 1523112883 |q (electronic bk.) | ||
| 020 | |z 9781785610950 | ||
| 020 | |z 1785610953 | ||
| 035 | |a (OCoLC)1011182292 |z (OCoLC)1008972655 |z (OCoLC)1012864750 |z (OCoLC)1036294095 |z (OCoLC)1055826929 |z (OCoLC)1087424505 |z (OCoLC)1097103777 |z (OCoLC)1197630285 | ||
| 245 | 0 | 0 | |a Mobile biometrics / |c edited by Guodong Guo and Harry Wechsler. |
| 264 | 1 | |a London : |b Institution of Engineering and Technology, |c 2017. | |
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a IET security series ; |v 03 | |
| 490 | 1 | |a The IET book series on advances in biometrics | |
| 504 | |a Includes bibliographical references and 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 is about the use of biometrics on mobile/smart phones. An integrated and informative analysis, this is a timely survey of the state of the art research and developments in this rapidly growing area. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g 1. |t Mobile biometrics / |r Harry Wechsler -- |g 1.1. |t Introduction -- |g 1.2. |t Book organization -- |g 1.3. |t Acknowledgment -- |g 2. |t Mobile biometric device design: history and challenges / |r Michael Rathwell -- |g 2.1. |t Introduction -- |g 2.2. |t Biometrics -- |g 2.3. |t Fingerprint recognition and the first AFIS system -- |g 2.4. |t Mobile biometric devices -- |g 2.5. |t Features found on good mobile biometrics device design -- |g 2.5.1. |t User friendly, nice styling and ergonomics, light, and rugged -- |g 2.5.2. |t Consistently quick and easy capture of high-quality images -- |g 2.5.3. |t Easy, seamless integration to a back-end biometric system -- |g 2.5.4. |t Quick processing and fast responses -- |g 2.5.5. |t High accuracy, security and privacy -- |g 2.6. |t History of mobile biometric devices -- |g 2.6.1. |t Law enforcement market devices -- |g 2.6.2. |t Commercial/consumer market devices with biometric capabilities -- |g 2.7. |t Future and challenges -- |t References -- |g 3. |t Challenges in developing mass-market mobile biometric sensors / |r Richard K. Fenrich -- |g 3.1. |t Background discussion -- |g 3.1.1. |t Use cases -- |g 3.1.2. |t Biometric sensors -- |g 3.1.3. |t New product development -- |g 3.2. |t primary challenges -- |g 3.2.1. |t Market relevance -- |g 3.2.2. |t Research and development -- |g 3.2.3. |t Manufacturing -- |g 3.2.4. |t Integration -- |g 3.2.5. |t Support -- |g 3.2.6. |t Higher level considerations -- |g 3.3. |t Conclusion -- |t References -- |g 4. |t Deep neural networks for mobile person recognition with audio-visual signals / |r F. Sohel -- |g 4.1. |t Biometric systems -- |g 4.1.1. |t What is biometrics? -- |g 4.1.2. |t Multimodal biometrics -- |g 4.2. |t Audio-visual biometric systems -- |g 4.2.1. |t Preprocessing -- |g 4.2.2. |t Feature extraction -- |g 4.2.3. |t Classification -- |g 4.2.4. |t Fusion -- |g 4.2.5. |t Audio-visual corporation -- |g 4.3. |t Mobile person recognition -- |g 4.3.1. |t Speaker recognition systems -- |g 4.3.2. |t Face recognition systems -- |g 4.3.3. |t Audio-visual person recognition on MOBIO -- |g 4.4. |t Deep neural networks for person recognition -- |g 4.4.1. |t DBN-DNN for unimodal person recognition -- |g 4.4.2. |t DBM-DNN for person recognition -- |g 4.5. |t Summary -- |t References -- |g 5. |t Active authentication using facial attributes / |r Rama Chellappa -- |g 5.1. |t Introduction -- |g 5.2. |t Facial attribute classifiers -- |g 5.2.1. |t Linear attribute classifiers -- |g 5.2.2. |t Convolutional neural network attribute model -- |g 5.2.3. |t Performance of the attribute classifiers -- |g 5.3. |t Authentication -- |g 5.3.1. |t Short-term authentication -- |g 5.3.2. |t Long-term authentication -- |g 5.3.3. |t Discussion -- |g 5.4. |t Platform implementation feasibility -- |g 5.4.1. |t Memory -- |g 5.4.2. |t Computation efficiency and power consumption -- |g 5.5. |t Summary and discussion -- |t Acknowledgments -- |t References -- |g 6. |t Fusion of shape and texture features for lip biometry in mobile devices / |r Sambit Bakshi -- |g 6.1. |t Introduction -- |g 6.1.1. |t Evolution of lip as biometric trait -- |g 6.1.2. |t Why lip among other biometric traits? -- |g 6.1.3. |t Biometric authentication for handheld devices -- |g 6.1.4. |t Suitability of lip biometric for handheld devices -- |g 6.2. |t Motivation -- |g 6.3. |t Anatomy of lip biometric system -- |g 6.3.1. |t HMM-based modelling -- |g 6.3.2. |t Training, testing, and inferences through HMM -- |g 6.4. |t Experimental verification and results -- |g 6.4.1. |t Assumptions and constraints in the experiment -- |g 6.4.2. |t Databases used -- |g 6.4.3. |t Parameters of evaluation -- |g 6.4.4. |t Results and analysis -- |g 6.5. |t Conclusions -- |t References -- |g 7. |t Mobile device usage data as behavioral biometrics / |r Aaron D. Striegel -- |g 7.1. |t Introduction -- |g 7.2. |t Biometric system modules -- |g 7.3. |t Data collection -- |g 7.4. |t Feature extraction -- |g 7.4.1. |t Name-based features -- |g 7.4.2. |t Positional features -- |g 7.4.3. |t Touch features -- |g 7.4.4. |t Voice features -- |g 7.5. |t Research approaches -- |g 7.5.1. |t Application traffic -- |g 7.5.2. |t Text -- |g 7.5.3. |t Movement -- |g 7.5.4. |t Touch -- |g 7.5.5. |t Multimodal approaches -- |g 7.6. |t Research challenges -- |g 7.7. |t Summary -- |t References -- |g 8. |t Continuous mobile authentication using user-phone interaction / |r Ioannis A. |
| 505 | 0 | 0 | |t Kakadiaris -- |g 8.1. |t Introduction -- |g 8.2. |t Previous works -- |g 8.2.1. |t Touch gesture-based mobile authentication -- |g 8.2.2. |t Keystroke-based mobile authentication -- |g 8.3. |t Touch gesture features -- |g 8.4. |t User authentication schema overview -- |g 8.5. |t Dynamic time warping-based method -- |g 8.5.1. |t One nearest neighbor-dynamic time warping -- |g 8.5.2. |t Sequential recognition -- |g 8.5.3. |t Multistage filtering with dynamic template adaptation -- |g 8.5.4. |t Experimental results -- |g 8.6. |t Graphic touch gesture-based method -- |g 8.6.1. |t Feature extraction -- |g 8.6.2. |t Statistical touch dynamics images -- |g 8.6.3. |t User authentication algorithms -- |g 8.6.4. |t Experimental results -- |g 8.7. |t Virtual key typing-based method -- |g 8.7.1. |t Feature extraction -- |g 8.7.2. |t User authentication -- |g 8.7.3. |t Experiment results -- |g 8.8. |t Conclusion -- |t Acknowledgments -- |t References -- |g 9. |t Smartwatch-based gait biometrics / |r Andrew Johnston -- |g 19.1. |t Introduction -- |g 9.2. |t Smartwatch hardware -- |g 9.3. |t Biometric tasks: identification and authentication -- |g 9.3.1. |t identification -- |g 9.3.2. |t Authentication -- |g 9.4. |t Data preprocessing -- |g 9.4.1. |t Segmentation -- |g 9.4.2. |t Segment selection -- |g 9.5. |t Selecting a feature set -- |g 9.5.1. |t Statistical features -- |g 9.5.2. |t Histogram-based features -- |g 9.5.3. |t Cycle-based features -- |g 9.5.4. |t Time domain -- |g 9.5.5. |t Summary -- |g 9.6. |t System evaluation and testing -- |g 9.6.1. |t Selecting an evaluation metric -- |g 9.6.2. |t Single-instance evaluation and voting schemes -- |g 9.7. |t Template aging: an implementation challenge -- |g 9.8. |t Conclusion -- |t References -- |g 10. |t Toward practical mobile gait biometrics / |r Yunbin Deng -- |t Abstract -- |g 10.1. |t Introduction -- |g 10.2. |t Related work -- |g 10.3. |t GDI gait representation -- |g 10.3.1. |t Gait dynamics images -- |g 10.3.2. |t Pace-compensated gait dynamics images -- |g 10.4. |t Gait identity extraction using i-vectors -- |g 10.5. |t Performance analysis -- |g 10.5.1. |t McGill University naturalistic gait dataset -- |g 10.5.2. |t Osaka University largest gait dataset -- |g 10.5.3. |t Mobile dataset with multiple walking speed -- |g 10.6. |t Conclusions and future work -- |t Acknowledgments -- |t References -- |g 11. |t 4F["!-ID: mobile four-fingers biometrics system / |r Hector Hoyos -- |g 11.1. |t Introduction -- |g 11.2. |t Related work -- |g 11.2.1. |t Finger segmentation (ROI localization) -- |g 11.2.2. |t Image preprocessing and enhancement -- |g 11.2.3. |t Feature extraction and matching -- |g 11.2.4. |t System deployment -- |g 11.3. |t 4F["!-ID system -- |g 11.3.1. |t 4F["!-ID image acquisition -- |g 11.3.2. |t 4F["!-ID image segmentation -- |g 11.3.3. |t 4F["!-ID image preprocessing -- |g 11.3.4. |t Feature extraction and matching -- |g 11.4. |t Experimental results -- |g 11.5. |t Summary -- |t References -- |g 12. |t Palmprint recognition on mobile devices / |r Lu Leng -- |g 12.1. |t Background -- |g 12.2. |t Current authentication technologies on mobile devices -- |g 12.2.1. |t Knowledge-authentication -- |g 12.2.2. |t Biometric-authentication -- |g 12.3. |t Mobile palmprint recognition framework -- |g 12.3.1. |t Introduction on palmprint -- |g 12.3.2. |t Strengths of mobile palmprint -- |g 12.3.3. |t Palmprint recognition framework -- |g 12.4. |t Palmprint acquirement modes -- |g 12.4.1. |t Offline mode -- |g 12.4.2. |t Online mode -- |g 12.5. |t Palmprint acquirement and preprocessing -- |g 12.5.1. |t Preprocessing in contact mode -- |g 12.5.2. |t Preprocessing in contactless mode -- |g 12.5.3. |t Acquirement and preprocessing in mobile mode -- |g 12.6. |t Palmprint feature extraction and matching -- |g 12.7. |t Conclusions and development trends -- |t Acknowledgments -- |t References -- |g 13. |t Addressing the presentation attacks using periocular region for smartphone biometrics / |r Christoph Busch -- |g 13.1. |t Introduction -- |g 13.2. |t Database -- |g 13.2.1. |t MobiLive 2014 Database -- |g 13.2.2. |t PAVID Database -- |g 13.3. |t Vulnerabilities towards presentation attacks -- |g 13.3.1. |t Vulnerability analysis using the PAVID -- |g 13.4. |t PAD techniques -- |g 13.4.1. |t Metrics for PAD algorithms -- |g 13.4.2. |t Texture features for PAD -- |g 13.5. |t Experiments and results -- |g 13.5.1. |t Results on MoblLive 2014 database -- |g 13.5.2. |t Results on the PAVID database -- |g 13.6. |t Discussions and conclusion -- |t Acknowledgments -- |t References -- |g 14. |t Countermeasures to face photo spoofing attacks by exploiting structure and texture information from rotated face sequences / |r Stan Z. Li -- |g 14.1. |t Introduction -- |g 14.2. |t Related works -- |g 14.3. |t Overview of the proposed method -- |g 14.4. |t Sparse 3D facial structure recovery -- |g 14.4.1. |t Initial recovery from two images -- |g 14.4.2. |t Facial structure refinement -- |g 14.4.3. |t Key frame selection -- |g 14.5. |t Face anti-spoofing classification -- |g 14.5.1. |t Structure-based anti-spoofing classifier -- |g 14.5.2. |t Texture-based anti-spoofing classifier -- |g 14.6. |t Experiments -- |g 14.6.1. |t Database description -- |g 14.6.2. |t Evaluation protocols -- |g 14.6.3. |t Results of structure-based method -- |g 14.6.4. |t Results of texture-based method -- |g 14.6.5. |t Combination of structure and texture clues -- |g 14.6.6. |t Computational cost analysis -- |g 14.7. |t Conclusion -- |t References -- |g 15. |t Biometric antispoofing on mobile devices / |r Gian Luca Foresti -- |g 15.1. |t Introduction -- |g 15.2. |t Biometric antispoofing -- |g 15.2.1. |t State-of-the-art in face antispoofing -- |g 15.2.2. |t State-of-the-art in fingerprint antispoofing -- |g 15.2.3. |t State-of-the-art in iris antispoofing -- |g 15.3. |t Case study: MoBio_LivDet system -- |g 15.3.1. |t Experiments -- |g 15.4. |t Research opportunities -- |g 15.4.1. |t Mobile liveness detection -- |g 15.4.2. |t Mobile biometric spoofing databases -- |g 15.4.3. |t Generalization to unknown attacks -- |g 15.4.4. |t Randomizing input biometric data. |
| 505 | 0 | 0 | |g Note continued: |g 15.4.5. |t Fusion of biometric system and countermeasures -- |g 15.5. |t Conclusion -- |t References -- |g 16. |t Biometric open protocol standard / |r Hector Hoyos -- |g 16.1. |t Introduction -- |g 16.2. |t Overview -- |g 16.2.1. |t Scope -- |g 16.2.2. |t Purpose -- |g 16.2.3. |t Intended audience -- |g 16.3. |t Definitions, acronyms, and abbreviations -- |g 16.3.1. |t Definitions -- |g 16.3.2. |t Acronyms and abbreviations -- |g 16.4. |t Conformance -- |g 16.5. |t Security considerations -- |g 16.5.1. |t Background -- |g 16.5.2. |t Genesis -- |g 16.5.3. |t Enrollment -- |g 16.5.4. |t Matching agreement -- |g 16.5.5. |t Role gathering -- |g 16.5.6. |t Access control -- |g 16.5.7. |t Auditing and assurance -- |g 16.6. |t BOPS interoperability -- |g 16.6.1. |t Application -- |g 16.6.2. |t Registration -- |g 16.6.3. |t Prevention of replay -- |g 16.7. |t Summary -- |t Further Reading -- |g 17. |t Big data and cloud identity service for mobile authentication / |r Nalini K. Ratha -- |g 17.1. |t Introduction -- |g 17.1.1. |t Identity establishment and management -- |g 17.1.2. |t Mega trend impacts -- |g 17.1.3. |t Large-scale biometric applications and big data -- |g 17.1.4. |t Cloud computing -- |g 17.2. |t Characteristics of mobile biometrics -- |g 17.2.1. |t Mobile biometric concepts -- |g 17.2.2. |t Mobile biometric data -- |g 17.2.3. |t Biometric processes and performance metrics -- |g 17.3. |t Smart mobile devices -- |g 17.3.1. |t Many mobile sensors available -- |g 17.3.2. |t Multibiometrics fusion -- |g 17.4. |t Emerging mobile biometrics techniques -- |g 17.4.1. |t Traditional biometrics -- fingerprint, face, and iris -- |g 17.4.2. |t Behavior biometrics -- |g 17.4.3. |t Risk-based continuous authentication and trust management -- |g 17.5. |t Conceptual mobile application architecture -- |g 17.6. |t Biometric identity services in the cloud -- |g 17.6.1. |t Biometrics-enabled identity services -- |g 17.6.2. |t Biometric identity service cloud model -- |g 17.6.3. |t How to develop a biometrics-identity-service-cloud model? -- |g 17.7. |t Cognitive authentication system: a point of view -- |g 17.8. |t Conclusions -- |t References -- |g 18. |t Outlook for mobile biometrics / |r Harry Wechsler. |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Biometric identification. | |
| 650 | 0 | |a Computer security. | |
| 650 | 0 | |a Mobile computing. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Guo, Guodong, |e editor. | |
| 700 | 1 | |a Wechsler, Harry, |d 1948- |e editor. |1 https://id.oclc.org/worldcat/entity/E39PBJk933HhWbHMXVmTqmwjYP | |
| 776 | 0 | 8 | |t Mobile biometrics. |d London : The Institution of Engineering and Technology, 2017 |z 9781785610950 |w (OCoLC)968776781 |
| 830 | 0 | |a IET security series ; |v 3. | |
| 830 | 0 | |a IET book series on advances in biometrics. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpMB000012/mobile-biometrics?kpromoter=marc |y Full text |
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