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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]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Human recognition in unconstrained environments : |b using computer vision, pattern recognition and machine learning methods for biometrics / |c edited by Maria De Marsico, Michele Nappi, Hugo Proen�ca. |
| 264 | 1 | |a London : |b Academic Press, an imprint of Elsevier, |c [2017] | |
| 264 | 4 | |c �2017 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed January 25, 2017). | |
| 520 | |a 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. -- |c Edited summary from book. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 650 | 0 | |a Biometric identification. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Pattern Recognition, Automated |0 (DNLM)D010363 | |
| 650 | 6 | |a Identification biom�etrique. |0 (CaQQLa)201-0365086 | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) |0 (CaQQLa)201-0028094 | |
| 650 | 6 | |a Vision par ordinateur. |0 (CaQQLa)201-0074889 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Biometric identification. |2 fast |0 (OCoLC)fst00832607 | |
| 650 | 7 | |a Computer vision. |2 fast |0 (OCoLC)fst00872687 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Pattern recognition systems. |2 fast |0 (OCoLC)fst01055266 | |
| 700 | 1 | |a De Marsico, Maria, |e editor. | |
| 700 | 1 | |a Nappi, Michele, |e editor. | |
| 700 | 1 | |a Proen�ca, Hugo, |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics. |d Amsterdam, [Netherlands] : Elsevier, �2017 |h xvi, 231 pages |z 9780081007051 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780081007051 |z Texto completo |