Human Recognition in Unconstrained Environments

Human Recognition in Unconstrained Environments

Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics

1st Edition - January 9, 2017

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  • Editors: Maria De Marsico, Michele Nappi, Hugo Proença
  • Hardcover ISBN: 9780081007051
  • eBook ISBN: 9780081007129

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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

Key Features

  • 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


University and industry R&D Engineers researching pattern recognition, computer vision and machine learning methods applied to biometric system development

Table of Contents

    • Contributors
    • Editor Biographies
    • Foreword
    • Chapter 1: Unconstrained Data Acquisition Frameworks and Protocols
      • Abstract
      • 1.1. Introduction
      • 1.2. Unconstrained Biometric Data Acquisition Modalities
      • 1.3. Typical Challenges
      • 1.4. Unconstrained Biometric Data Acquisition Systems
      • 1.5. Conclusions
      • References
    • Chapter 2: Face Recognition Using an Outdoor Camera Network
      • Abstract
      • 2.1. Introduction
      • 2.2. Taxonomy of Camera Networks
      • 2.3. Face Association in Camera Networks
      • 2.4. Face Recognition in Outdoor Environment
      • 2.5. Outdoor Camera Systems
      • 2.6. Remaining Challenges and Emerging Techniques
      • 2.7. Conclusions
      • References
    • Chapter 3: Real Time 3D Face-Ear Recognition on Mobile Devices: New Scenarios for 3D Biometrics “in-the-Wild”
      • Abstract
      • 3.1. Introduction
      • 3.2. 3D Capture of Face and Ear: CURRENT Methods and Suitable Options
      • 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
    • Chapter 4: A Multiscale Sequential Fusion Approach for Handling Pupil Dilation in Iris Recognition
      • Abstract
      • 4.1. Introduction
      • 4.2. Previous Work
      • 4.3. WVU Pupil Light Reflex (PLR) Dataset
      • 4.4. Impact of Pupil Dilation
      • 4.5. Proposed Method
      • 4.6. Experimental Results
      • 4.7. Conclusions and Future Work
      • References
    • Chapter 5: Iris Recognition on Mobile Devices Using Near-Infrared Images
      • Abstract
      • 5.1. Introduction
      • 5.2. Preprocessing
      • 5.3. Feature Analysis
      • 5.4. Multimodal Biometrics
      • 5.5. Conclusions
      • References
    • Chapter 6: Fingerphoto Authentication Using Smartphone Camera Captured Under Varying Environmental Conditions
      • Abstract
      • Acknowledgements
      • 6.1. Introduction
      • 6.2. Literature Survey
      • 6.3. IIITD SmartPhone Fingerphoto Database v1
      • 6.4. Proposed Fingerphoto Matching Algorithm
      • 6.5. Experimental Results
      • 6.6. Conclusion
      • 6.7. Future Work
      • References
    • Chapter 7: Soft Biometric Attributes in the Wild: Case Study on Gender Classification
      • Abstract
      • 7.1. Introduction
      • 7.2. Biometrics in the Wild
      • 7.3. Gender Classification in the Wild
      • 7.4. Conclusions
      • References
    • Chapter 8: Gait Recognition: The Wearable Solution
      • Abstract
      • 8.1. Machine Vision Approach
      • 8.2. Floor Sensor Approach
      • 8.3. Wearable Sensor Approach
      • 8.4. Datasets Available for Experiments
      • 8.5. An Example of a Complete System for Gait Recognition
      • 8.6. Conclusions
      • References
    • Chapter 9: Biometric Authentication to Access Controlled Areas Through Eye Tracking
      • Abstract
      • 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
    • Chapter 10: Noncooperative Biometrics: Cross-Jurisdictional Concerns
      • Abstract
      • 10.1. Introduction
      • 10.2. Biometrics for Implementing Biometric Surveillance
      • 10.3. Reaction to Public Opinion
      • 10.4. The Early Days
      • 10.5. An Interesting Clue (2007)
      • 10.6. Biometric Surveillance Today
      • 10.7. Conclusions
      • References
    • Index

Product details

  • No. of pages: 248
  • Language: English
  • Copyright: © Academic Press 2017
  • Published: January 9, 2017
  • Imprint: Academic Press
  • Hardcover ISBN: 9780081007051
  • eBook ISBN: 9780081007129

About the Editors

Maria De Marsico

Maria De Marsico is Associate Professor at Sapienza University of Rome, Department of Computes Science. She got her Master degree in Computer science from University of Salerno. Her scientific interests focus on Image Processing and Human Computer Interaction. Regarding the first one, she works on biometric recognition, including face, iris, gate, and multimodal recognition. Regarding the second one, she is especially interested in multimodal interaction, accessibility for users with special needs, and advanced techniques for personalized distance learning. She is Associate Editor of Pattern Recognition Letters, and Area Editor of the IEEE Biometrics Compendium. She published about 100 scientific works in international journals, conferences, and book chapters. She has been member of many Technical program Committees and is referee for several top journals, and Program Chair for the International Conference on Pattern Recognition Applications and Methods since 2013.

Affiliations and Expertise

Associate Professor of Computer Science, Sapienza University of Rome, Italy

Michele Nappi

Michele Nappi received the laurea degree (cum laude) in computer science from the University of Salerno, Salerno, Italy, in 1991, the M.Sc. degree in information and communication technology from I.I.A.S.S. "E.R. Caianiello", Vietri sul Mare, Salerno, and the Ph.D. degree in applied mathematics and computer science from the University of Padova, Padova, Italy. He is currently an Associate Professor of computer science at the University of Salerno.

His research interests include Multibiometric Systems, Pattern Recognition, Image Processing, Compression and Indexing, Multimedia Databases, Human-Computer Interaction, VR/AR. He co-authored over 120 papers in international conference, peer review journals and book chapters in these fields (see He also served as Guest Editor for several international journals and as Editor for International Books. In 2014 He was one of the founders of the spin off BS3 (Biometric System for Security and Safety). President of the Italian Chapter of the IEEE Biometrics Council (2015-2017), member of IAPR and IEEE, He is team leader of the Biometric and Image Processing Lab (BIPLAB). Dr. Nappi received several international awards for scientific and research activities.

Affiliations and Expertise

Associate Professor of Computer Science, University of Salerno, Italy

Hugo Proença

Hugo Proença
Hugo Proença B.Sc. (2001), M.Sc. (2004), Ph.D. (2007) and Habilitation (Agregado, 2016) is an Associate Professor and the current Head of the Department (2015-2017) of Computer Science, University of Beira Interior. He has been researching mainly about biometrics and visual-surveillance, particularly in developing human recognition solutions able to work in degraded data, resulting from unconstrained data acquisition protocols. He is an associate editor of the Image and Vision Computing Journal, the coordinating editor of the IEEE Biometrics Council Newsletter and the area editor (ocular biometrics) of the IEEE Biometrics Compendium Journal. Also, he is a member of the Editorial Board of the International Journal of Biometrics and served as Guest Editor of special issues of the Pattern Recognition Letters, Image and Vision Computing and Signal, Image and Video Processing journals.

Affiliations and Expertise

Associate Professor, Department of Computer Science, University of Beira Interior, Portugal

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