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Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics /
Providing a unique picture of the complete in-the-wild biometric recognition processing chain, this book covers everything from data acquisition through to detection, segmentation, encoding, and matching reactions against security incidents. --
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press, an imprint of Elsevier,
[2017]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Front Cover
- Human Recognition in Unconstrained Environments
- Copyright
- Contents
- Contributors
- Editor Biographies
- Foreword
- 1 Unconstrained Data Acquisition Frameworks and Protocols
- 1.1 Introduction
- 1.2 Unconstrained Biometric Data Acquisition Modalities
- 1.3 Typical Challenges
- 1.3.1 Optical Constraints
- 1.3.2 Non-comprehensive View of the Scene
- 1.3.3 Out-of-Focus
- 1.3.4 Calibration of Multi-camera Systems
- 1.4 Unconstrained Biometric Data Acquisition Systems
- 1.4.1 Low Resolutions Systems
- 1.4.2 PTZ-Based Systems
- 1.4.3 Face
- 1.5 Conclusions
- References
- 2 Face Recognition Using an Outdoor Camera Network
- 2.1 Introduction
- 2.2 Taxonomy of Camera Networks
- 2.2.1 Static Camera Networks
- 2.2.2 Active Camera Networks
- 2.2.3 Characteristics of Camera Networks
- 2.3 Face Association in Camera Networks
- 2.3.1 Face-to-Face Association
- 2.3.2 Face-to-Person Association
- 2.4 Face Recognition in Outdoor Environment
- 2.4.1 Robust Descriptors for Face Recognition
- 2.4.2 Video-Based Face Recognition
- 2.4.3 Multi-view and 3D Face Recognition
- 2.4.4 Face Recognition with Context Information
- 2.4.5 Incremental Learning of Face Recognition
- 2.5 Outdoor Camera Systems
- 2.5.1 Static Camera Approach
- 2.5.2 Single PTZ Camera Approach
- 2.5.3 Master and Slave Camera Approach
- 2.5.4 Distributed Active Camera Networks
- 2.6 Remaining Challenges and Emerging Techniques
- 2.7 Conclusions
- References
- 3 Real Time 3D Face-Ear Recognition on Mobile Devices: New Scenarios for 3D Biometrics "in-the-Wild
- 3.1 Introduction
- 3.2 3D Capture of Face and Ear: CURRENT Methods and Suitable Options
- 3.2.1 Laser Scanners
- 3.2.2 Structured Light Scanners
- 3.2.3 Stereophotogrammetry
- 3.3 Mobile Devices for Ubiquitous Face-Ear Recognition.
- 3.4 The Next Step: Mobile Devices for 3D Sensing Aiming at 3D Biometric Applications
- 3.5 Conclusions and Future Scenarios
- References
- 4 A Multiscale Sequential Fusion Approach for Handling Pupil Dilation in Iris Recognition
- 4.1 Introduction
- 4.1.1 Pupil Dilation
- 4.1.2 Layout
- 4.2 Previous Work
- 4.2.1 Pupil Dilation
- 4.2.2 Bit Matching
- 4.3 WVU Pupil Light Re ex (PLR) Dataset
- 4.4 Impact of Pupil Dilation
- 4.5 Proposed Method
- 4.5.1 IrisCode Generation
- 4.5.2 Typical IrisCode Matcher
- 4.5.3 Multi- lter Matching Patterns
- 4.5.4 Proposed IrisCode Matcher
- 4.6 Experimental Results
- 4.7 Conclusions and Future Work
- References
- 5 Iris Recognition on Mobile Devices Using Near-Infrared Images
- 5.1 Introduction
- 5.2 Preprocessing
- 5.3 Feature Analysis
- 5.4 Multimodal Biometrics
- 5.5 Conclusions
- References
- 6 Fingerphoto Authentication Using Smartphone Camera Captured Under Varying Environmental Conditions
- 6.1 Introduction
- 6.2 Literature Survey
- 6.3 IIITD SmartPhone Fingerphoto Database v1
- 6.3.1 Set 1: Background Variation
- 6.3.2 Set 2: Illumination Variation
- 6.3.3 Set 3: Live-Scan Fingerprints
- 6.4 Proposed Fingerphoto Matching Algorithm
- 6.4.1 Fingerphoto Segmentation
- 6.4.2 Fingerphoto Enhancement (Enh#1)
- 6.4.3 LBP Based Enhancement (Enh#2)
- 6.4.4 Scattering Network Based Feature Representation
- 6.4.5 Matching Techniques
- 6.5 Experimental Results
- 6.5.1 Performance of the Proposed Matching Pipeline
- 6.5.2 Comparison of Matching Algorithms
- 6.5.3 Comparison of Distance Metrics
- 6.5.4 Effect of Enhancement
- 6.6 Conclusion
- 6.7 Future Work
- Acknowledgements
- References
- 7 Soft Biometric Attributes in the Wild: Case Study on Gender Classi cation
- 7.1 Introduction
- 7.2 Biometrics in the Wild
- 7.3 Gender Classi cation in the Wild
- 7.3.1 Datasets.
- 7.3.2 Proposals Summary
- 7.3.3 Discussion
- 7.4 Conclusions
- References
- 8 Gait Recognition: The Wearable Solution
- 8.1 Machine Vision Approach
- 8.2 Floor Sensor Approach
- 8.3 Wearable Sensor Approach
- 8.3.1 The Accelerometer Sensor
- 8.4 Datasets Available for Experiments
- 8.5 An Example of a Complete System for Gait Recognition
- 8.6 Conclusions
- References
- 9 Biometric Authentication to Access Controlled Areas Through Eye Tracking
- 9.1 Introduction
- 9.2 ATM-Like Solutions
- 9.3 Methods Based on Fixation and Scanpath Analysis
- 9.4 Methods Based on Eye/Gaze Velocity
- 9.5 Methods Based on Pupil Size
- 9.6 Methods Based on Oculomotor Features
- 9.7 Methods Based on Head Orientation
- 9.8 Conclusions
- References
- 10 Noncooperative Biometrics: Cross-Jurisdictional Concerns
- 10.1 Introduction
- 10.2 Biometrics for Implementing Biometric Surveillance
- 10.3 Reaction to Public Opinion
- 10.3.1 Geopolitical Context
- 10.3.2 Technological Skills
- 10.3.3 Proportionality
- 10.3.4 A Particular Operational Framework
- 10.4 The Early Days
- 10.4.1 Commercial Context
- 10.4.2 Historical Context
- 10.4.3 Social Context, the Newham and Ybor City Experiments
- 10.5 An Interesting Clue (2007)
- 10.6 Biometric Surveillance Today
- 10.6.1 Increased Perception of Insecurity
- 10.6.2 Getting Used to the Erosion of Privacy
- 10.6.3 Increase of Mobility
- 10.7 Conclusions
- References
- Index
- Back Cover.