Handbook of Face Recognition
This highly anticipated new edition provides a thorough account of face recognition research and technology, spanning detection, tracking, alignment, feature extraction, recognition technologies and issues in evaluation, systems, security and applications.
1135369391
Handbook of Face Recognition
This highly anticipated new edition provides a thorough account of face recognition research and technology, spanning detection, tracking, alignment, feature extraction, recognition technologies and issues in evaluation, systems, security and applications.
249.99 In Stock
Handbook of Face Recognition

Handbook of Face Recognition

Handbook of Face Recognition

Handbook of Face Recognition

Hardcover(2nd ed. 2011)

$249.99 
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Overview

This highly anticipated new edition provides a thorough account of face recognition research and technology, spanning detection, tracking, alignment, feature extraction, recognition technologies and issues in evaluation, systems, security and applications.

Product Details

ISBN-13: 9780857299314
Publisher: Springer London
Publication date: 08/31/2011
Edition description: 2nd ed. 2011
Pages: 699
Product dimensions: 6.00(w) x 9.20(h) x 1.70(d)

About the Author

Dr. Stan Z. Li is Professor at the National Laboratory of Pattern Recognition, Director of the Center for Biometrics and Security Research, and Director of the R&D Center for Visual Internet of Things, within the Chinese Academy of Sciences.

Dr. Anil K. Jain is University Distinguished Professor in the Department of Computer Science and Engineering at Michigan State University, U.S.A.

Table of Contents

Part I: Introduction and Background.- 1. Overview on Face recognition.- 2. Historical Developments and Challenges.- 3. Applications.- Part II: Fundamentals of Deep Neural Networks.- 4. Overview on Deep Learning for FR.- 5. Deep Neural Network Architecture Design.- 6. Loss Function Design.- 7. Auto-Encoders.- 8. Convolutional Neural Networks.- 9. Generative Adversarial Networks.- 10. Transfer Learning and Domain Adaptation.- 11. Deep Learning with Big/Small Data.- 12. Model Compression and Speedup.- 13. Programming Platforms for Deep Learning.- Part III: Face Recognition by Deep Neural Networks.- 14. Overview on Face Recognition Methods.- 15. Preprocessing Methods.- 16. Face Localization Detection.- 17. Face Localization Landmark.- 18. Visual Face Recognition.- 19. Multispectral Face Recognition.- 20. Fusion for Face Recognition.
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