http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
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.
edited by Guodong Guo and Harry Wechsler.
Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
Includes bibliographical references and index.
London : The Institution of Engineering and Technology, 2017
IET book series on advances in biometrics