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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.

Detalles Bibliográficos
Clasificación:	Libro Electrónico
Otros Autores:	Guo, Guodong (Editor ), Wechsler, Harry, 1948- (Editor )
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	London : Institution of Engineering and Technology, 2017.
Colección:	IET security series ; 3. IET book series on advances in biometrics.
Temas:	Biometric identification. Computer security. Mobile computing. Computer Security Identification biométrique. Sécurité informatique. Informatique mobile. NATURE > Reference. SCIENCE > Life Sciences > Biology. SCIENCE > Life Sciences > General. biometrics (access control) cloud computing. mobile computing. security of data. Conference papers and proceedings.
Acceso en línea:	Texto completo

Tabla de Contenidos:

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.

Mobile biometrics /

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