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[null,"1.","Record Nr.",null,"UNINA9910583060803321"]
[null,null,"Autore",null,"De Marsico Maria"]
[null,null,"Titolo",null,"Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics / / [edited by] Maria De Marsico, Michele Nappi, Hugo Pedro Proença"]
[null,null,"Pubbl/distr/stampa",null,"London, United Kingdom : , : Academic Press, , [2017]"]
[{"$nulls":4},"©2017"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (1 volume) : illustrations"]
[null,null,"Disciplina",null,"006.248"]
[null,null,"Soggetti",null,"Biometric identification"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Pattern recognition systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front Cover -- Human Recognition in Unconstrained Environments -- Copyright -- Contents -- Contributors -- Editor Biographies -- Foreword -- 1 Unconstrained Data Acquisition Frameworks and Protocols -- 1.1 Introduction -- 1.2 Unconstrained Biometric Data Acquisition Modalities -- 1.3 Typical Challenges -- 1.3.1 Optical Constraints -- 1.3.2 Non-comprehensive View of the Scene -- 1.3.3 Out-of-Focus -- 1.3.4 Calibration of Multi-camera Systems -- 1.4 Unconstrained Biometric Data Acquisition Systems -- 1.4.1 Low Resolutions Systems -- 1.4.2 PTZ-Based Systems -- 1.4.3 Face -- 1.5 Conclusions -- References -- 2 Face Recognition Using an Outdoor Camera Network -- 2.1 Introduction -- 2.2 Taxonomy of Camera Networks -- 2.2.1 Static Camera Networks -- 2.2.2 Active Camera Networks -- 2.2.3 Characteristics of Camera Networks -- 2.3 Face Association in Camera Networks -- 2.3.1 Face-to-Face Association -- 2.3.2 Face-to-Person Association -- 2.4 Face Recognition in Outdoor Environment -- 2.4.1 Robust Descriptors for Face Recognition -- 2.4.2 Video-Based Face Recognition -- 2.4.3 Multi-view and 3D Face Recognition -- 2.4.4 Face Recognition with Context Information -- 2.4.5 Incremental Learning of Face Recognition -- 2.5 Outdoor Camera Systems -- 2.5.1 Static Camera Approach -- 2.5.2 Single PTZ Camera Approach -- 2.5.3 Master and Slave Camera Approach -- 2.5.4 "]
[{"$nulls":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."]
[{"$nulls":4},"3.4 The Next Step: Mobile Devices for 3D Sensing Aiming at 3D Biometric Applications -- 3.5 Conclusions and Future Scenarios -- References -- 4 A Multiscale Sequential Fusion Approach for Handling Pupil Dilation in Iris Recognition -- 4.1 Introduction -- 4.1.1 Pupil Dilation -- 4.1.2 Layout -- 4.2 Previous Work -- 4.2.1 Pupil Dilation -- 4.2.2 Bit Matching -- 4.3 WVU Pupil Light Re ex (PLR) Dataset -- 4.4 Impact of Pupil Dilation -- 4.5 Proposed Method -- 4.5.1 IrisCode Generation -- 4.5.2 Typical IrisCode Matcher -- 4.5.3 Multi- lter Matching Patterns -- 4.5.4 Proposed IrisCode Matcher -- 4.6 Experimental Results -- 4.7 Conclusions and Future Work -- References -- 5 Iris Recognition on Mobile Devices Using Near-Infrared Images -- 5.1 Introduction -- 5.2 Preprocessing -- 5.3 Feature Analysis -- 5.4 Multimodal Biometrics -- 5.5 Conclusions -- References -- 6 Fingerphoto Authentication Using Smartphone Camera Captured Under Varying Environmental Conditions -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 IIITD SmartPhone Fingerphoto Database v1 -- 6.3.1 Set 1: Background Variation -- 6.3.2 Set 2: Illumination Variation -- 6.3.3 Set 3: Live-Scan Fingerprints -- 6.4 Proposed Fingerphoto Matching Algorithm -- 6.4.1 Fingerphoto Segmentation -- 6.4.2 Fingerphoto Enhancement (Enh#1) -- 6.4.3 LBP Based Enhancement (Enh#2) -- 6.4.4 Scattering Network Based Feature Representation -- 6.4.5 Matching Techniques -- 6.5 Experimental Results -- 6.5.1 Performance of the Proposed Matching Pipeline -- 6.5.2 Comparison of Matching Algorithms -- 6.5.3 Comparison of Distance Metrics -- 6.5.4 Effect of Enhancement -- 6.6 Conclusion -- 6.7 Future Work -- Acknowledgements -- References -- 7 Soft Biometric Attributes in the Wild: Case Study on Gender Classi cation -- 7.1 Introduction -- 7.2 Biometrics in the Wild -- 7.3 Gender Classi cation in the Wild -- 7.3.1 Datasets."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Increase of Mobility -- 10.7 Conclusions -- References -- Index -- Back Cover."]
[null,null,null,"Sommario/riassunto","Human Recognition in Unconstrained Environments provides a unique picture of the complete ‘in-the-wild’ biometric recognition processing chain; from data acquisition through to detection, segmentation, encoding, and matching reactions against security incidents.  Coverage includes: Data hardware architecture fundamentals Background subtraction of humans in outdoor scenes Camera synchronization Biometric traits: Real-time detection and data segmentation Biometric traits: Feature encoding / matching Fusion at different levels Reaction against security incidents Ethical issues in non-cooperative biometric recognition in public spaces With this book readers will learn how to:  Use computer vision, pattern recognition and machine learning methods for biometric recognition in real-world, real-time settings, especially those related to forensics and security Choose the most suited biometric traits and recognition methods for uncontrolled settings Evaluate the performance of a biometric system on real world data Presents a complete picture of the biometric recognition processing chain, ranging from data acquisition to the reaction procedures against security incidents Provides specific requirements and issues behind each typical phase of the development of a robust biometric recognition system Includes a contextualization of the ethical/privacy issues behind the development of a covert recognition system which can be used for forensics and security activities"]