Machine-based Intelligent Face Recognition

• Main Author: Mou, Dengpan
• Format: eBook
• Language: English
• Published: Berlin, Heidelberg Springer Berlin / Heidelberg 2011
• Edition: 1
• Subjects: Human face recognition (Computer science)
• ISBN: 9783642007507; 3642007503
• DOI: 10.1007/978-3-642-00751-4

This book discusses the general engineering method of imitating intelligent human brains for video-based face recognition in a fundamental way. It overviews state-of-the-art research on this subject, and especially the advances in face recognition.

Table of Contents:

• Intro -- Title Page -- Copyright Page -- Preface -- Acknowledgements -- Table of Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Face Recognition-Machine Versus Human -- 1.2 Proposed Approach -- 1.3 Prospective Applications -- 1.3.1 Recognition in the Future Intelligent Home -- 1.3.2 Automotive -- 1.3.3 Mobile Phone for Children -- 1.4 Outline -- References -- 2 Fundamentals and Advances in Biometrics and Face Recognition -- 2.1 Generalized Biometric Recognition -- 2.2 Cognitive-based Biometric Recognition -- 2.2.1 Introduction -- 2.2.2 History of Cognitive Science -- 2.2.3 Human Brain Structure -- 2.2.4 Generic Methods in Cognitive Science -- 2.2.5 Visual Function in Human Brain -- 2.2.6 General Cognitive-based Object Recognition -- 2.2.7 Cognitive-based Face Recognition -- 2.2.8 Inspirations from Cognitive-based Face Recognition -- 2.3 Machine-based Biometric Recognition -- 2.3.1 Introduction -- 2.3.2 Biometric Recognition Tasks -- 2.3.3 Enrollment-a Special Biometric Procedure -- 2.3.4 Biometric Methods Overview -- 2.3.5 Fingerprint Recognition -- 2.4 Generalized Face Recognition Procedure -- 2.5 Machine-based Face Detection -- 2.5.1 Face Detection Categories -- 2.6 Machine-based Face Tracking -- 2.7 Machine-based Face Recognition -- 2.7.1 Overview -- 2.7.2 Benchmark Studies of Face Recognition -- 2.7.3 Some General Terms Used in Face Recognition -- 2.7.4 Recognition Procedures and Methods -- 2.7.5 Video-based Recognition -- 2.7.6 Unsupervised and Fully Automatic Approaches -- 2.8 Summary and Discussions -- References -- 3 Combined Face Detection and Tracking Methods -- 3.1 Introduction -- 3.2 Image-based Face Detection -- 3.2.1 Choice of the Detection Algorithm -- 3.2.2 Overview of the Detection Algorithm -- 3.2.3 Face Region Estimation -- 3.3 Temporal-based Face Detection -- 3.3.1 Overview
• 3.3.2 Search Region Estimation -- 3.3.3 Analysis of Temporal Changes -- 3.4 Summary -- 3.5 Further Discussions -- References -- 4 Automatic Face Recognition -- 4.1 Overview -- 4.2 Feature Extraction and Encoding -- 4.3 Matching/Classification -- 4.3.1 Image-based Classifier -- 4.3.2 Adaptive Similarity Threshold -- 4.3.3 Temporal Filtering -- 4.4 Combined Same Face Decision Algorithms -- 4.5 Summary -- References -- 5 Unsupervised Face Database Construction -- 5.1 Introduction -- 5.2 Backgrounds for Constructing Face Databases -- 5.2.1 Supervised Learning -- 5.2.2 Unsupervised Learning -- 5.2.3 Clustering Analysis -- 5.3 Database Structure -- 5.3.1 A Fused Clustering Method -- 5.3.2 Parameters in the Proposed Structure -- 5.4 Features of an Optimum Database -- References -- 6 State Machine Based Automatic Procedure -- 6.1 Introduction -- 6.2 States Explorations -- 7 System Implementation -- 7.1 Introduction -- 7.2 Typical Hardware Configuration -- 7.3 Software Implementation -- 7.3.1 Overview -- 7.3.2 Implementation Efforts -- 7.4 Technology Dependent Parameters -- 7.5 Summary -- References -- 8 Performance Analysis -- 8.1 Introduction -- 8.2 Performance of Face Detection -- 8.3 Performance of Face Recognition -- 8.4 Performance of Database Construction Algorithms -- 8.5 Overall Performance of the Whole System -- 8.5.1 Online Version -- 8.5.2 Offline Version -- 8.5.3 Critical Assumptions -- References -- 8.6 Summary -- 9 Conclusions and Future Directions -- 9.1 Conclusions -- 9.2 Future Directions -- Index