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Human Recognition in Unconstrained Environments: Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics

Editat de Maria De Marsico, Michele Nappi, Hugo Pedro Proença
en Limba Engleză Hardback – 13 ian 2017
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
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Specificații

ISBN-13: 9780081007051
ISBN-10: 0081007051
Pagini: 248
Dimensiuni: 191 x 235 x 19 mm
Greutate: 2.04 kg
Editura: ELSEVIER SCIENCE

Public țintă

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

Cuprins

1. Iris Recognition on Mobile Devices Using Near-Infrared Images
2. Face recognition using dictionary learning and domain adaptation
3. Periocular Recognition in Non-ideal Images
4. Real Time 3D Face-Ear Recognition on Mobile Devices: New Scenarios for 3D Biometricks “in-the-Wild”
5. Fingerphoto Recognition in Outdoor Environment using Smartphones
6. Soft biometric labels in the wild. Case study on gender classification
7. Unconstrained data acquisition frameworks and protocols
8. Biometric Authentication to Access Controlled Areas through Eye Tracking
9. Non-cooperative biometrics: Cross-Jurisdictional concerns
10. Pattern Recognition and Machine Learning Methods for assessing the quality of fingerprints