Human Recognition in Unconstrained Environments

Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics
Herausgegeben:De Marsico, Maria; Nappi, Michele; Proença, Hugo Pedro

• Gebundenes Buch

Produktbeschreibung

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

Produktdetails

• Verlag: Academic Press
• Artikelnr. des Verlages: 61703493
• Seitenzahl: 248
• Englisch
• Abmessung: 15mm x 191mm x 235mm
• Gewicht: 651g
• ISBN-13: 9780081007051
• ISBN-10: 0081007051
• Artikelnr.: 45367737

Autorenporträt

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.

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.

Inhaltsangabe

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