{
"item_title" : "Human Recognition in Unconstrained Environments",
"item_author" : [" Maria de Marsico", "Michele Nappi", "Hugo Pedro Proença "],
"item_description" : "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 fundamentalsBackground subtraction of humans in outdoor scenesCamera synchronizationBiometric traits: Real-time detection and data segmentationBiometric traits: Feature encoding / matchingFusion at different levelsReaction against security incidentsEthical issues in non-cooperative biometric recognition in public spacesWith 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 securityChoose the most suited biometric traits and recognition methods for uncontrolled settingsEvaluate the performance of a biometric system on real world data",
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Human Recognition in Unconstrained Environments : Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics
Overview
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
This item is Non-Returnable
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Details
- ISBN-13: 9780081007051
- ISBN-10: 0081007051
- Publisher: Academic Press
- Publish Date: January 2017
- Dimensions: 9.6 x 7.5 x 0.7 inches
- Shipping Weight: 4.49 pounds
- Page Count: 248
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