Human Recognition in Unconstrained Environments

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

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