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Title Mobile biometrics / edited by Guodong Guo and Harry Wechsler
Published London : Institution of Engineering and Technology, 2017
©2017

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