人脸识别技术手册：第二版

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"Handbook of Face Recognition (English) Second Edition" 是一本关于人脸识别技术的专业书籍，由Stan Z. Li和Anil K. Jain编辑。这本书是最新版本，专注于介绍当前最先进的人脸识别技术，对研究人员具有很高的参考价值。书中可能涵盖了人脸识别的基础理论、算法以及实际应用。在标签中提到的关键技术包括： 1. **PCA（主成分分析）**：PCA是一种常用的数据降维方法，在人脸识别中用于提取面部特征，减少数据的复杂性，同时保留关键信息。 2. **LBP（局部二值模式）**：LBP是一种纹理描述符，用于捕获图像的局部特性，尤其适用于人脸图像的灰度变化分析，常用于人脸检测和识别。 3. **Fisherface**：Fisherface是一种基于Fisher线性判别分析的特征提取方法，旨在找到能最大化类间距离和最小化类内距离的特征向量，从而提高人脸识别的准确性。 4. **KLT（Kanade-Lucas-Tomasi）追踪器**：KLT追踪器是一种光流估计方法，用于跟踪图像序列中目标的位置和形状，对于人脸识别中的动态人脸跟踪有重要作用。部分内容虽然没有提供详细的章节或技术细节，但可以推测本书可能包含以下主题： - 人脸识别的基本概念和历史背景 - 面部图像处理，包括预处理、对齐和标准化 - 特征提取技术，如PCA、LBP和Fisherface的详细解释与比较 - 人脸检测和识别的算法，可能包括Haar特征、Adaboost、HOG等 - 基于深度学习的人脸识别方法，如卷积神经网络（CNN）的应用 - 人脸识别系统的评估和性能指标 - 实际应用案例，如安全监控、移动设备解锁、社交媒体验证等 - 对未来人脸识别技术发展的展望和挑战本书不仅适合人脸识别领域的研究人员和开发者，也对相关领域的学者、工程师和研究生提供了深入理解人脸识别技术的宝贵资源。通过阅读此书，读者可以掌握当前人脸识别领域的核心技术和最新进展。

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Contributors xv

Pavan Turaga Department of Electrical and Computer Engineering, Center for

Automation Research, University of Maryland, College Park, MD 20742, USA,

pturaga@umiacs.umd.edu

Thomas Vetter Department of Mathematics and Computer Science, University of

Basel, Bernoullistrasse 16, 4056 Basel, Switzerland, thomas.vetter@unibas.ch

Christian Wallraven Max Planck Institute for Biological Cybernetics, Spe-

mannstrasse 38, 72076 Tübingen, Germany, wallraven@korea.ac.kr; Department

of Brain and Cognitive Engineering, Korea University, Seoul, Korea

Liting Wang State Key Laboratory of Intelligent Technology and Systems, Ts-

inghua National Laboratory for Information Science and Technology, Depart-

ment of Electronic Engineering, Tsinghua University, Beijing 100084, China,

wangltmail@tsinghua.edu.cn

Yang Wang Carnegie Mellon University, Pittsburgh, PA 15213, USA,

wangy@cs.cmu.edu

Frederick W. Wheeler Visualization and Computer Vision Lab, GE Global Re-

search, Niskayuna, NY 12309, USA, wheeler@ge.com

Jianxin Wu School of Computer Engineering, Nanyang Technological University,

Singapore, Singapore, jxwu@ntu.edu.sg

Ziyou Xiong United Technologies Research Center, East Hartford, CT 06108,

USA, xiongz@utrc.utc.com

Yi Yao Visualization and Computer Vision Lab, GE Global Research, Niskayuna,

NY 12309, USA, yi.yao@ge.com

Dong Yi Center for Biometrics and Security Research & National Laboratory of

Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Bei-

jing, China, dyi@cbsr.ia.ac.cn

Zhenqiu Zhang University of Illinois at Urbana-Champaign, Urbana, IL 61801,

USA, zzhang6@uiuc.edu

Shaohua Kevin Zhou Siemens Corporate Research, 755 College Road East,

Princeton, NJ 08540, USA, kzhou@scr.siemens.com

Chapter 1

Introduction

Stan Z. Li and Anil K. Jain

1.1 Face Recognition

Face recognition is a task that humans perform routinely and effortlessly in our daily

lives. Wide availability of powerful and low-cost desktop and embedded computing

systems has created an enormous interest in automatic processing of digital images

in a variety of applications, including biometric authentication, surveillance, human-

computer interaction, and multimedia management. Research and development in

automatic face recognition follows naturally.

Face recognition has several advantages over other biometric modalities such as

ﬁngerprint and iris: besides being natural and nonintrusive, the most important ad-

vantage of face is that it can be captured at a distance and in a covert manner. Among

the six biometric attributes considered by Hietmeyer [16], facial features scored the

highest compatibility in a Machine Readable Travel Documents (MRTD) [27]sys-

tem based on a number of evaluation factors, such as enrollment, renewal, machine

requirements, and public perception, shown in Fig. 1.1. Face recognition, as one

of the major biometric technologies, has become increasingly important owing to

rapid advances in image capture devices (surveillance cameras, camera in mobile

phones), availability of huge amounts of face images on the Web, and increased

demands for higher security.

The ﬁrst automated face recognition system was developed by Takeo Kanade

in his Ph.D. thesis work [18] in 1973. There was a dormant period in automatic

face recognition until the work by Sirovich and Kirby [19, 38] on a low dimen-

S.Z. Li (



)

Center for Biometrics and Security Research & National Laboratory of Pattern Recognition,

Institute of Automation, Chinese Academy of Sciences, Beijing, China

e-mail: szli@cbsr.ia.ac.cn

A.K. Jain

Michigan State University, East Lansing, MI 48824, USA

e-mail: jain@cse.msu.edu

S.Z. Li, A.K. Jain (eds.), Handbook of Face Recognition,

DOI 10.1007/978-0-85729-932-1_1, © Springer-Verlag London Limited 2011

2 S.Z. Li and A.K. Jain

Fig. 1.1 A scenario of using biometric MRTD systems for passport control (left), and a compar-

ison of various biometric traits based on MRTD compatibility (right, from Hietmeyer [16]with

permission)

sional face representation, derived using the Karhunen–Loeve transform or Princi-

pal Component Analysis (PCA). It is the pioneering work of Turk and Pentland on

Eigenface [42] that reinvigorated face recognition research. Other major milestones

in face recognition include: the Fisherface method [3, 12], which applied Linear

Discriminant Analysis (LDA) after a PCA step to achieve higher accuracy; the use

of local ﬁlters such as Gabor jets [21, 45] to provide more effective facial features;

and the design of the AdaBoost learning based cascade classiﬁer architecture for

real time face detection [44].

Face recognition technology is now signiﬁcantly advanced since the time when

the Eigenface method was proposed. In the constrained situations, for example

where lighting, pose, stand-off, facial wear, and facial expression can be controlled,

automated face recognition can surpass human recognition performance, especially

when the database (gallery) contains a large number of faces.

However, automatic

face recognition still faces many challenges when face images are acquired under

unconstrained environments. In the following sections, we give a brief overview of

the face recognition process, analyze technical challenges, propose possible solu-

tions, and describe state-of-the-art performance.

This chapter provides an introduction to face recognition research. Main steps of

face recognition processing are described. Face detection and recognition problems

are explained from a face subspace viewpoint. Technology challenges are identiﬁed

and possible strategies for solving some of the problems are suggested.

1.2 Categorization

As a biometric system, a face recognition system operates in either or both of two

modes: (1) face veriﬁcation (or authentication), and (2) face identiﬁcation (or recog-

nition). Face veriﬁcation involves a one-to-one match that compares a query face

Most individuals can identify only a few thousand people in real life.

1 Introduction 3

image against an enrollment face image whose identity is being claimed. Person

veriﬁcation for self-serviced immigration clearance using E-passport is one typical

application.

Face identiﬁcation involves one-to-many matching that compares a query face

against multiple faces in the enrollment database to associate the identity of the

query face to one of those in the database. In some identiﬁcation applications, one

just needs to ﬁnd the most similar face. In a watchlist check or face identiﬁcation

in surveillance video, the requirement is more than ﬁnding most similar faces; a

conﬁdence level threshold is speciﬁed and all those faces whose similarity score is

above the threshold are reported.

The performance of a face recognition system largely depends on a variety of

factors such as illumination, facial pose, expression, age span, hair, facial wear, and

motion. Based on these factors, face recognition applications may be divided into

two broad categories in terms of a user’s cooperation: (1) cooperative user scenarios

and (2) noncooperative user scenarios.

The cooperative case is encountered in applications such as computer login,

physical access control, and e-passport, where the user is willing to be coopera-

tive by presenting his/her face in a proper way (for example, in a frontal pose with

neutral expression and eyes open) in order to be granted the access or privilege.

In the noncooperative case, which is typical in surveillance applications, the user

is unaware of being identiﬁed. In terms of distance between the face and the camera,

near ﬁeld face recognition (less than 1 m) for cooperative applications (e.g., access

control) is the least difﬁcult problem, whereas far ﬁeld noncooperative applications

(e.g., watchlist identiﬁcation) in surveillance video is the most challenging.

Applications in-between the above two categories can also be foreseen. For ex-

ample, in face-based access control at a distance, the user is willing to be coopera-

tive but he is unable to present the face in a favorable condition with respect to the

camera. This may present challenges to the system even though such cases are still

easier than identifying the identity of the face of a subject who is not cooperative.

However, in almost all of the cases, ambient illumination is the foremost challenge

for most face recognition applications.

1.3 Processing Workﬂow

Face recognition is a visual pattern recognition problem, where the face, represented

as a three-dimensional object that is subject to varying illumination, pose, expres-

sion, and other factors, needs to be identiﬁed based on acquired images. While two-

dimensional face images are commonly used in most applications, certain applica-

tions requiring higher levels of security demand the use of three-dimensional (depth

or range) images or optical images beyond the visual spectrum. A face recognition

system generally consists of four modules as depicted in Fig. 1.2: face localiza-

tion, normalization, feature extraction, and matching. These modules are explained

below.

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