基于机器的智能人脸识别 目录

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

基于机器的智能人脸识别 本书特色

《基于机器的智能人脸识别》是由高等教育出版社出版的。

基于机器的智能人脸识别 节选

《基于机器的智能人脸识别》内容简介：Machine, based Intelligent Face Recognition discusses the general engineering method of imitating intelligent human brains for video-based face recognition in a fundamental way, which is completely unsupervised,automatic, self-learning,self-updated and robust. It also overviews stateof-the-art researchon cognitive-based biometrics and machine-based biometrics, and especially the advances in face recognition.This book is intended for scientists, researchers, engineers, and students in the field of computer vision, machine intelligence, and particularly of face recognition.

基于机器的智能人脸识别 相关资料

插图：Although the two parties who hold opposite opinions provide us much in-formation for the face recognition in cortex, further cognitive research ishighly demanding for ending the debates and providing us a clearer answer.However, we, although as researchers in a different field, can now still figureout that, each side has unfortunately one limitation in common: the importanceof frontal lobe is not taken into consideration at all. As mentioned earlier, thefrontal lobe contributes to the high-level analysis such as reasoning, planning,and problem-solving, etc. Frontal lobe is performing the most complicatedtask, being expected to be involved in all brain process, and hence demonstrating the fundamental intelligence. This region should be definitely explored forthe face recognition procedure. In early 1990s, Gross [29] suggests that theface processing cells are extended to the frontal lobe. In reality, this study focuses on finding the visual ability of the frontal lobe rather than the intelligence of it. More recently, Mechelli et al. [30] and Johnson et al. [31] foundout that, the face processing task, although mainly performed in posterior cortical regions such as FFA, OFA and fSTS, is modulated by top-down signalsoriginating in prefrontal cortex. The main purpose in [31] is to point out that,refreshing is a component of more complex modulatory operations such asworking memory and mental imagery. And the refresh related activity maythus be involved in the common activation patterns seen across different cognitive tasks. In summary, most researches are still concentrating on specificand different prospects. However, they convincingly support our fundamentalopinion: the high-level intelligence performed in frontal lobe is crucial for facerecognition.It is important to note that, there is a high level research on cognitivebased face recognition, published by P. Sinha et al. [32]. They reporte

基于机器的智能人脸识别 作者简介

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.