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

Human Recognition in Unconstrained Environments: Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics provides a unique picture of the complete ‘in-the-wild’ biometric recognition processing chain, from data acquisition to detection, segmentation, encoding, and matching reactions against security incidents. It also surveys the processing phases that support the recognition process and which of those, in practice, enables a system to work, including data hardware architecture fundamentals, background subtraction of humans in outdoor scenes, camera synchronization, and biometric traits, including real-time detection and data segmentation.

In addition, the concept of biometric traits like feature encoding and matching and decision, fusion at different levels, reaction against security incidents, and ethical issues in non-cooperative biometric recognition in public spaces are discussed.

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, and 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