| 《基于机器的智能人脸识别》是由高等教育出版社出版的。 |
| Dr. Dengpan Mou,Dr.-Ing. and MSc from University of Ulm, Germany,is with Harman/Bedger Automotive Systems GmbH as technology expert,working on video processing, computer vision, machine learning and other research and development topics. |
| 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 3.2.4 Face Detection Quality 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 Offiine Version 8.5.3 Critical Assumptions 8.6 Summary References 9 Conclusions and Future Directions 9.1 Conclusions 9.2 Future Directions Index |
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