基於機器的智慧人臉識別
本書摘自: 中國圖書網 www.bookschina.com.tw
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《基於機器的智慧人臉識別》是由高等教育出版社出版的。
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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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《基於機器的智慧人臉識別》內
容簡介: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.
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插圖: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
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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. |
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