Pattern Recognition Technologies and Applications

Pattern Recognition

Technologies and

Applications:

Recent Advances

Brijesh Verma

Central Queensland University, Australia

Michael Blumenstein

Griffith University, Australia

Hershey • New York

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Library of Congress Cataloging-in-Publication Data

Pattern recognition technologies and applications : recent advances / Brijesh Verma and Michael Blumenstein, editors.

p. cm.

Summary: “ This book provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their

field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech

recognition, signature verification, and gender classification”--Provided by publisher.

Includes bibliographical references and index.

ISBN-13: 978-1-59904-807-9 (hardcover)

ISBN-13: 978-1-59904-809-3 (e-book)

1. Pattern perception. 2. Pattern perception--Data processing. I. Verma, Brijesh. II. Blumenstein, Michael.

Q327.P383 2008

006.4--dc22

2007037396

British Cataloguing in Publication Data

A Cataloguing in Publication record for this book is available from the British Library.

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the publisher.

If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating

the library's complimentary electronic access to this publication.

Preface ................................................................................................................................................xiii

Acknowledgment .............................................................................................................................xviii

Chapter I

Fusion of Segmentation Strategies for Off-Line Cursive Handwriting Recognition ............................. 1

Brijesh Verma, Central Queensland University, Australia

Michael Blumenstein, Griffith University, Australia

Chapter II

Elastic Matching Techniques for Handwritten Character Recognition ............................................... 17

Seiichi Uchida, Kyushu University, Japan

Chapter III

State of the Art in Off-Line Signature Verification .............................................................................. 39

Luana Batista, École de technologie supérieure, Canada

Dominique Rivard, École de technologie supérieure, Canada

Robert Sabourin, École de technologie supérieure, Canada

Eric Granger, École de technologie supérieure, Canada

Patrick Maupin, Defence Research and Development Canada (DRDC), Canada

Chapter IV

An Automatic Off-Line Signature Verification and Forgery Detection System .................................. 63

Vamsi Krishna Madasu, Queensland University of Technology, Australia

Brian C. Lovell, NICTA Limited (Queensland Laboratory),

and University of Queensland, Australia

Chapter V

Introduction to Speech Recognition ..................................................................................................... 90

Sergio Suárez-Guerra, National Polytechnic Institute, Mexico

Jose Luis Oropeza-Rodriguez, National Polytechnic Institute, Mexico

Table of Contents

Chapter VI

Seeking Patterns in the Forensic Analysis of Handwriting and Speech ............................................ 110

Graham Leedham, Griffith University, Australia

Vladimir Pervouchine, University of New South Wales (Asia), Singapore

Haishan Zhong, Nanyang Technological University, Singapore

Chapter VII

Image Pattern Recognition-Based Morphological Structure and Applications ................................. 140

Donggang Yu, Bioinformatics Applications Research Centre, James Cook University,

Australia

Tuan D. Pham, Bioinformatics Applications Research Centre, James Cook University,

Australia

Hong Yan, City University of Hong Kong, Hong Kong

Chapter VIII

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System ........... 188

Ting Shan, National ICT Australia, and The University of Queensland, Australia

Abbas Bigdeli, National ICT Australia, Australia

Brian C. Lovell, National ICT Australia, and The University of Queensland, Australia

Shaokang Chen, National ICT Australia, and The University of Queensland, Australia

Chapter IX

Occlusion Sequence Mining for Activity Discovery from Surveillance Videos ............................... 212

Prithwijit Guha, Indian Institute of Technology - Kanpur, India

Amitabha Mukerjee, Indian Institute of Technology - Kanpur, India

K.S. Venkatesh, Indian Institute of Technology - Kanpur, India

Chapter X

Human Detection in Static Images .................................................................................................... 227

Hui-Xing Jia, Tsinghua University - Beijing, China

Yu-Jin Zhang, Tsinghua University - Beijing, China

Chapter XI

A Brain-Inspired Visual Pattern Recognition Architecture and Its Applications ............................... 244

Fok Hing Chi Tivive, Member, IEEE, and University of Wollongong, Australia

Abdesselam Bouzerdoum, Senior Member, IEEE, and University of Wollongong, Australia

Chapter XII

Significance of Logic Synthesis in FPGA-Based Design of Image

and Signal Processing Systems .......................................................................................................... 265

Mariusz Rawski, Warsaw University of Technology, Poland

Henry Selvaraj, University of Nevada, USA

Bogdan J. Falkowski, Nanyang Technological University, Singapore

Tadeusz Łuba, Warsaw University of Technology, Poland

Chapter XIII

A Novel Support Vector Machine with Class-Dependent Features for Biomedical Data ................. 284

Nina Zhou, Nanyang Technological University, Singapore

Lipo Wang, Nanyang Technological University, Singapore

Chapter XIV

A Unified Approach to Support Vector Machines .............................................................................. 299

Alistair Shilton, The University of Melbourne, Australia

Marimuthu Palaniswami, The University of Melbourne, Australia

Chapter XV

Cluster Ensemble and Multi-Objective Clustering Methods ............................................................. 325

Katti Faceli, Federal University of São Carlos, Brazil

Andre C.P.L.F. de Carvalho, University of São Paulo, Brazil

Marcilio C.P. de Souto, Federal University of Rio Grande do Norte, Brazil

Chapter XVI

Implementing Negative Correlation Learning in Evolutionary Ensembles

with Suitable Speciation Techniques ................................................................................................. 344

Peter Duell, The Centre of Excellence for Research in Computational Intelligence

and Applications (CERCIA), University of Birmingham, UK

Xin Yao, The Centre of Excellence for Research in Computational Intelligence

and Applications (CERCIA), University of Birmingham, UK

Chapter XVII

A Recurrent Probabilistic Neural Network for EMG Pattern Recognition ........................................ 370

Toshio Tsuji, Hiroshima University, Japan

Nan Bu, Hiroshima University, Japan

Osamu Fukuda, National Institute of Advanced Industrial Science and Technology, Japan

Compilation of References .............................................................................................................. 388

About the Contributors ................................................................................................................... 424

Index ................................................................................................................................................ 433

Preface ................................................................................................................................................xiii

Acknowledgment .............................................................................................................................xviii

Chapter I

Fusion of Segmentation Strategies for Off-Line Cursive Handwriting Recognition ............................. 1

Brijesh Verma, Central Queensland University, Australia

Michael Blumenstein, Griffith University, Australia

Cursive handwriting recognition is a challenging task for many real-world applications such as document

authentication, form processing, postal address recognition, reading machines for the blind, bank cheque

recognition, and interpretation of historical documents. Therefore, in the last few decades, researchers

have put enormous effort into developing various techniques for handwriting recognition. This chapter

reviews existing handwriting recognition techniques and presents the current state of the art in cursive

handwriting recognition. The chapter also presents segmentation strategies and a segmentation-based

approach for automated recognition of unconstrained cursive handwriting. The chapter provides a com￾prehensive literature with basic and advanced techniques and research results in handwriting recognition

for graduate students and also for advanced researchers.

Chapter II

Elastic Matching Techniques for Handwritten Character Recognition ............................................... 17

Seiichi Uchida, Kyushu University, Japan

This chapter reviews various elastic matching techniques for handwritten character recognition. Elastic

matching is formulated as an optimization problem of planar matching, or pixel-to-pixel correspondence,

between two character images under a certain matching model such as affine and nonlinear. Use of elastic

matching instead of rigid matching improves the robustness of recognition systems against geometric

deformations in handwritten character images. In addition, the optimized matching itself represents the

deformation of handwritten characters and thus is useful for statistical analysis of the deformation. This

chapter argues the general property of elastic matching techniques and their classification by match￾ing models and optimization strategies. It also argues various topics and future work related to elastic

matching for emphasizing theoretical and practical importance of elastic matching.

Detailed Table of Contents

Chapter III

State of the Art in Off-Line Signature Verification .............................................................................. 39

Luana Batista, École de technologie supérieure, Canada

Dominique Rivard, École de technologie supérieure, Canada

Robert Sabourin, École de technologie supérieure, Canada

Eric Granger, École de technologie supérieure, Canada

Patrick Maupin, Defence Research and Development Canada (DRDC), Canada

Automatic signature verification is a biometric method that can be applied in all situations where hand￾written signatures are used, such as cashing a check, signing a credit card, authenticating a document, and

others. Over the last two decades, several innovative approaches for off-line signature verification have

been introduced in literature. Therefore, this chapter presents a survey of the most important techniques

used for feature extraction and verification in this field. The chapter also presents strategies used to face

the problem of limited amount of data, as well as important challenges and research directions.

Chapter IV

An Automatic Off-Line Signature Verification and Forgery Detection System .................................. 63

Vamsi Krishna Madasu, Queensland University of Technology, Australia

Brian C. Lovell, NICTA Limited (Queensland Laboratory),

and University of Queensland, Australia

This chapter presents an off-line signature verification and forgery detection system based on fuzzy mod￾eling. The various handwritten signature characteristics and features are first studied and encapsulated

to devise a robust verification system. The verification of genuine signatures and detection of forgeries

is achieved via angle features extracted using a grid method. The derived features are fuzzified by an

exponential membership function, which is modified to include two structural parameters. The structural

parameters are devised to take account of possible variations due to handwriting styles and to reflect other

factors affecting the scripting of a signature. The efficacy of the proposed system is tested on a large

database of signatures comprising more than 1,200 signature images obtained from 40 volunteers.

Chapter V

Introduction to Speech Recognition ..................................................................................................... 90

Sergio Suárez-Guerra, National Polytechnic Institute, Mexico

Jose Luis Oropeza-Rodriguez, National Polytechnic Institute, Mexico

This chapter presents the state of the art in Automatic Speech Recognition (ASR) technology, a very suc￾cessful technology in the computer science field related to multiple disciplines such as signal processing

and analysis, mathematical statistics, applied artificial intelligence, linguistics, and so forth. The unit of

essential information used to characterize the speech signal in the most widely used ASR systems is the

phoneme. However, several researchers recently have questioned this representation and demonstrated

the limitations of the phonemes, suggesting that ASR, which is better performance, can be developed,

replacing the phoneme by triphones and syllables as the unit of essential information used to character￾ize the speech signal. This chapter presents an overview of the most successful techniques used in ASR

systems together with some recently proposed ASR systems that intend to improve the characteristics

of conventional ASR systems.

Chapter VI

Seeking Patterns in the Forensic Analysis of Handwriting and Speech ............................................ 110

Graham Leedham, Griffith University, Australia

Vladimir Pervouchine, University of New South Wales (Asia), Singapore

Haishan Zhong, Nanyang Technological University, Singapore

This chapter examines features of handwriting and speech and their effectiveness at determining whether

the identity of a writer or speaker can be identified from handwriting or speech. For handwriting, some

of the subjective and qualitative features used by document examiners are investigated in a scientific and

quantitative manner based on analysis of three characters (d, y, and f) and the grapheme th. For speech,

several frequently used features are compared for their strengths and weaknesses in distinguishing

speakers. The results show that some features do have good discriminative power, while others are less

effective. Acceptable performance can be obtained in many situations using these features. However,

the effect of handwriting forgery/disguise or conscious speech imitation/alteration on these features

is not investigated. New and more powerful features are needed in the future if high accuracy person

identification can be achieved in the presence of disguise or forgery.

Chapter VII

Image Pattern Recognition-Based Morphological Structure and Applications ................................. 140

Donggang Yu, Bioinformatics Applications Research Centre, James Cook University,

Australia

Tuan D. Pham, Bioinformatics Applications Research Centre, James Cook University,

Australia

Hong Yan, City University of Hong Kong, Hong Kong

This chapter describes a new pattern recognition method: pattern recognition-based morphological struc￾ture. First, smooth following and linearization are introduced based on different chain codes. Second,

morphological structural points are described in terms of smooth followed contours and linearized lines,

and then the patterns of morphological structural points and their properties are given. Morphological

structural points are basic tools for pattern recognition-based morphological structure. Furthermore, we

discuss how the morphological structure can be used to recognize and classify images. One application

is document image processing and recognition, analysis, and recognition of broken handwritten digits.

Another one is dynamic analysis and recognition of cell-cycle screening based on morphological struc￾tures. Finally, a conclusion is given: advantage, disadvantage, and future research.

Chapter VIII

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System ........... 188

Ting Shan, National ICT Australia, and The University of Queensland, Australia

Abbas Bigdeli, National ICT Australia, Australia

Brian C. Lovell, National ICT Australia, and The University of Queensland, Australia

Shaokang Chen, National ICT Australia, and The University of Queensland, Australia

In this chapter, we propose a variability compensation technique that synthesizes realistic frontal face

images from nonfrontal views. It is based on modeling the face via Active Appearance Models and esti-

mating the pose through a correlation model. The proposed technique is coupled with Adaptive Principal

Component Analysis (APCA), which was previously shown to perform well in the presence of both

lighting and expression variations. The proposed recognition techniques, although advanced, are not

computationally intensive. So they are quite well suited to the embedded system environment. Indeed,

the authors have implemented an early prototype of a face recognition module on a mobile camera phone

so the camera could be used to identify the person holding the phone.

Chapter IX

Occlusion Sequence Mining for Activity Discovery from Surveillance Videos ............................... 212

Prithwijit Guha, Indian Institute of Technology - Kanpur, India

Amitabha Mukerjee, Indian Institute of Technology - Kanpur, India

K.S. Venkatesh, Indian Institute of Technology - Kanpur, India

Complex multi-object interactions result in occlusion sequences, which are a visual signature for the

event. In this work, multi-object interactions are tracked using a set of qualitative occlusion primitives

derived on the basis of the Persistence Hypothesis— objects continue to exist even when hidden from

view. Variable length temporal sequences of occlusion primitives are shown to be well correlated with

many classes of semantically significant events. In surveillance applications, determining occlusion

primitives is based on foreground blob tracking and requires no prior knowledge of the domain or camera

calibration. New foreground blobs are identified as putative objects, which may undergo occlusions,

split into multiple objects, merge back again, and so forth. Significant activities are identified through

temporal sequence mining, which bear high correlation with semantic categories (e.g., disembarking from

a vehicle involves a series of splits). Thus, semantically significant event categories can be recognized

without assuming camera calibration or any environment/object/action model priors.

Chapter X

Human Detection in Static Images .................................................................................................... 227

Hui-Xing Jia, Tsinghua University - Beijing, China

Yu-Jin Zhang, Tsinghua University - Beijing, China

Human detection is the first step for a number of applications such as smart video surveillance, driving

assistance system, and intelligent digital content management. It’s a challenging problem due to the

variance of illumination, color, scale, pose, and so forth. This chapter reviews various aspects of human

detection in static images and focuses on learning-based methods that build classifiers using training

samples. There are usually three modules for these methods: feature extraction, classifier design, and

merge of overlapping detections. The chapter reviews most of the existing methods for each module and

analyzes their respective pros and cons. The contribution includes two aspects: first, the performance of

existing feature sets on human detection are compared; second, a fast human detection system based on

Histogram of Oriented Gradients features and cascaded Adaboost classifier is proposed. This chapter

should be useful for both algorithm researchers and system designers in the computer vision and pattern

recognition community.

Chapter XI

A Brain-Inspired Visual Pattern Recognition Architecture and Its Applications ............................... 244

Fok Hing Chi Tivive, Member, IEEE, and University of Wollongong, Australia

Abdesselam Bouzerdoum, Senior Member, IEEE, and University of Wollongong, Australia

With the ever-increasing utilization of imagery in scientific, industrial, civilian, and military applications,

visual pattern recognition has been thriving as a research field and has become an essential enabling

technology for many applications. In this chapter, we present a brain-inspired pattern recognition ar￾chitecture that easily can be adapted to solve various real-world visual pattern recognition tasks. The

architecture has the ability to extract visual features from images and classify them within the same

network structure; in other words, it integrates the feature extraction stage with the classification stage,

and both stages are optimized with respect to one another. The main processing unit for feature extrac￾tion is governed by a nonlinear biophysical mechanism known as shunting inhibition, which plays a

significant role in visual information processing in the brain. Here, the proposed architecture is applied

to four real-world visual pattern recognition problems; namely, handwritten digit recognition, texture

segmentation, automatic face detection, and gender recognition. Experimental results demonstrate that

the proposed architecture is very competitive with and sometimes outperforms existing state-of-the-art

techniques for each application.

Chapter XII

Significance of Logic Synthesis in FPGA-Based Design of Image

and Signal Processing Systems .......................................................................................................... 265

Mariusz Rawski, Warsaw University of Technology, Poland

Henry Selvaraj, University of Nevada, USA

Bogdan J. Falkowski, Nanyang Technological University, Singapore

Tadeusz Łuba, Warsaw University of Technology, Poland

This chapter, taking FIR filters as an example, presents the discussion on efficiency of various imple￾mentation methodologies of DSP algorithms targeted at modern FPGA architectures. Nowadays, pro￾grammable technology provides the possibility to implement a digital system with the use of specialized

embedded DSP blocks. In the first place, however, this technology gives the designer the possibility

to increase efficiency of a designed system by exploitation of parallelisms of implemented algorithms.

Moreover, it is possible to apply special techniques such as distributed arithmetic (DA). Since in this ap￾proach general-purpose multipliers are replaced by combinational LUT blocks, it is possible to construct

digital filters of very high performance. Additionally, application of the functional decomposition-based

method to LUT blocks optimization and mapping has been investigated. The chapter presents results of

the comparison of various design approaches in these areas.

Chapter XIII

A Novel Support Vector Machine with Class-Dependent Features for Biomedical Data ................. 284

Nina Zhou, Nanyang Technological University, Singapore

Lipo Wang, Nanyang Technological University, Singapore

This chapter introduces an approach to class-dependent feature selection and a novel support vector

machine (SVM). The relative background and theory are presented for describing the proposed method,

and real applications of the method on several biomedical datasets are demonstrated in the end. The

authors hope that this chapter can provide readers a different view of feature selection method and also

the classifier so as to promote more promising methods and applications.

Chapter XIV

A Unified Approach to Support Vector Machines .............................................................................. 299

Alistair Shilton, The University Of Melbourne, Australia

Marimuthu Palaniswami, The University Of Melbourne, Australia

This chapter presents a unified introduction to support vector machine (SVM) methods for binary clas￾sification, one-class classification, and regression. The SVM method for binary classification (binary

SVC) is introduced first and then extended to encompass one-class classification (clustering). Next, us￾ing the regularized risk approach as a motivation, the SVM method for regression (SVR) is described.

These methods are then combined to obtain a single, unified SVM formulation that encompasses binary

classification, one-class classification, and regression (as well as some extensions of these), and the dual

formulation of this unified model is derived. A mechanical analogy for the binary and one-class SVCs

is given to give an intuitive explanation of the operation of these two formulations. Finally, the unified

SVM is extended to implement general cost functions, and an application of SVM classifiers to the

problem of spam e-mail detection is considered.

Chapter XV

Cluster Ensemble and Multi-Objective Clustering Methods ............................................................. 325

Katti Faceli, Federal University of São Carlos, Brazil

Andre C.P.L.F. de Carvalho, University of São Paulo, Brazil

Marcilio C.P. de Souto, Federal University of Rio Grande do Norte, Brazil

Clustering is an important tool for data exploration. Several clustering algorithms exist, and new algo￾rithms are frequently proposed in the literature. These algorithms have been very successful in a large

number of real-world problems. However, there is no clustering algorithm, optimizing only a single

criterion, able to reveal all types of structures (homogeneous or heterogeneous) present in a dataset. In

order to deal with this problem, several multi-objective clustering and cluster ensemble methods have

been proposed in the literature, including our multi-objective clustering ensemble algorithm. In this

chapter, we present an overview of these methods, which, to a great extent, are based on the combina￾tion of various aspects from traditional clustering algorithms.

Chapter XVI

Implementing Negative Correlation Learning in Evolutionary Ensembles

with Suitable Speciation Techniques ................................................................................................. 344

Peter Duell, The Centre of Excellence for Research in Computational Intelligence

and Applications (CERCIA), University of Birmingham, UK

Xin Yao, The Centre of Excellence for Research in Computational Intelligence

and Applications (CERCIA), University of Birmingham, UK

This chapter examines the motivation and characteristics of the NCL algorithm. Some recent work

relating to the implementation of NCL in a single objective evolutionary framework for classification

tasks is presented, and we examine the impact of two speciation techniques: implicit fitness sharing and

an island model population structure. The choice of such speciation techniques can have a detrimental

effect on the ability of NCL to produce accurate and diverse ensembles and should therefore be chosen

carefully. This chapter also provides an overview of other researchers’ work with NCL and gives some

promising future research directions.

Chapter XVII

A Recurrent Probabilistic Neural Network for EMG Pattern Recognition ........................................ 370

Toshio Tsuji, Hiroshima University, Japan

Nan Bu, Hiroshima University, Japan

Osamu Fukuda, National Institute of Advanced Industrial Science and Technology, Japan

In the field of pattern recognition, probabilistic neural networks (PNNs) have been proven as an impor￾tant classifier. For pattern recognition of EMG signals, the characteristics usually used are amplitude,

frequency, and space. However, a significant temporal characteristic exists in the transient and nonsta￾tionary EMG signals, which cannot be considered by traditional PNNs. In this chapter, a recurrent PNN

called Recurrent Log-Linearized Gaussian Mixture Network (R-LLGMN) is introduced for EMG pattern

recognition, with the emphasis on utilizing temporal characteristics. The structure of R-LLGMN is based

on the algorithm of a hidden Markov model (HMM), which is a routinely used technique for modeling

stochastic time series. Since R-LLGMN inherits advantages from both HMM and neural computation,

it is expected to have higher representation ability and show better performance when dealing with time

series like EMG signals. Experimental results show that R-LLGMN can achieve high discriminant ac￾curacy in EMG pattern recognition.

Compilation of References .............................................................................................................. 388

About the Contributors ................................................................................................................... 424

Index ................................................................................................................................................ 433

xiii

Preface

The history of automated pattern recognition can be traced back to the advent of modern computing mid￾way through the 20th century. Since that time, the popularity and growth of the pattern recognition field

has been fueled by its scientific significance and its applicability to the real world. Pattern recognition

is a very challenging and multidisciplinary research area attracting researchers and practitioners from

many fields, including computer science, computational intelligence, statistics, engineering, and medical

sciences, to mention just a few. Pattern recognition is a process described as retrieving a pattern from a

database of known patterns. It has numerous real-world applications in areas such as security, medicine,

information processing, and retrieval. Some pattern recognition applications in areas such as handwriting

recognition, document retrieval, speech recognition, signature verification, and face recognition are the

main focus of the current research activities in the pattern recognition and computational intelligence

communities around the globe. Researchers and developers are facing many challenges to applying

pattern recognition techniques in many real-world applications. This book consists of 17 peer-reviewed

chapters that describe theoretical and applied research work in this challenging area. The state of the art

in areas such as handwriting recognition, signature verification, speech recognition, human detection,

gender classification, morphological structures for image classification, logic synthesis for image and

signal processing, occlusion sequence mining, probabilistic neural networks for EMG patterns, multi￾objective clustering ensembles, evolutionary ensembles, support vector machines for biomedical data,

and unified support vector machines is presented in various chapters of this book.

The first two chapters focus on off-line cursive handwriting recognition. In Chapter I, Verma and

Blumenstein review existing handwriting recognition techniques and present the current state of the art

in cursive handwriting recognition. Standard handwriting recognition processes are presented, and each

process is described in detail. Some novel segmentation strategies and a segmentation-based approach

for automated recognition of unconstrained cursive handwriting are also presented.

In Chapter II, Uchida investigates the theoretical and practical importance of elastic matching for

handwriting recognition. He argues that the use of elastic matching techniques instead of rigid match￾ing techniques improves the robustness of handwriting recognition systems. In addition, the optimized

matching represents the deformation of handwritten characters and, thus, is useful for statistical analysis

of the deformation. Elastic matching is formulated as an optimization problem of planar matching, or

pixel-to-pixel correspondence, between two character images under a certain matching model such as

affine and nonlinear.

The next two chapters focus on off-line signature verification. In Chapter III, Batista, Rivard, Sab￾ourin, Granger, and Maupin present the current state of art in automatic signature verification. Automatic

signature verification is a biometric method that can be applied in all situations where handwritten sig￾natures are used, such as cashing a check, signing a credit card, and authenticating a document. They

review existing approaches in the literature and present a survey of the most important techniques used

for feature extraction and verification in this field. They also present strategies used for problems such

as limited amounts of data and show important challenges and some new research directions.

xiv

In Chapter IV, Madasu and Lovell present an off-line signature verification and forgery detection

system based on fuzzy modeling. The various handwritten signature characteristics and features are first

studied and encapsulated to devise a robust verification system. The verification of genuine signatures

and detection of forgeries is achieved via angle features extracted using a grid method. The derived

features are fuzzified by an exponential membership function, which is modified to include two struc￾tural parameters. The structural parameters are devised to take into account the possible variations due

to handwriting styles and to reflect other factors affecting the scripting of a signature. The proposed

system has been tested on a large database of signatures comprising more than 1,200 signature images

obtained from 40 volunteers.

Chapters V and VI focus on speech recognition. In Chapter V, Suárez-Guerra and Oropeza-Rodriguez

present the state of the art in automatic speech recognition. Speech recognition is very challenging for

researchers in many fields, including computer science, mathematical statistics, applied artificial intel￾ligence, and linguistics. The unit of essential information used to characterize the speech signal in the

most widely used ASR systems is the phoneme. However, several researchers recently have questioned

this representation and demonstrated the limitations of the phonemes, suggesting that ASR with better

performance can be developed replacing the phoneme by triphones and syllables as the unit of essential

information used to characterize the speech signal. This chapter presents an overview of the most suc￾cessful techniques used in ASR systems, together with some recently proposed ASR systems that intend

to improve the characteristics of conventional ASR systems.

In Chapter VI, Leedham, Pervouchine, and Zhong investigate features of handwriting and speech

and their effectiveness at determining whether the identity of a writer or speaker can be identified from

handwriting or speech. For handwriting, some of the subjective and qualitative features used by docu￾ment examiners are investigated in a scientific and quantitative manner based on the analysis of three

characters (d, y, and f) and the grapheme th. For speech, several frequently used features are compared

for their strengths and weaknesses in distinguishing speakers. The results show that some features do

have good discriminative power, while others are less effective. Acceptable performance can be obtained

in many situations using these features. However, the effect of handwriting forgery/disguise or conscious

speech imitation/alteration on these features is not investigated. New and more powerful features are

needed in the future if high accuracy person identification can be achieved in the presence of disguise

or forgery.

In Chapter VII, Yu, Pham, and Yan present a new pattern recognition method using morphological

structure. First, smooth linearization is introduced based on various chain codes. Second, morphological

structural points are described in terms of smooth followed contours and linearized lines, and then the

patterns of morphological structural points and their properties are given. Morphological structural points

are basic tools for pattern recognition-based morphological structure. Furthermore, how the morphologi￾cal structure can be used to recognize and classify images is presented. One application is document

image processing and recognition, analysis, and recognition of broken handwritten digits. Another one

is dynamic analysis and recognition of cell-cycle screening based on morphological structures.

In Chapter VIII, Shan, Bigdeli, Lovell, and Chen propose a variability compensation technique that

synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via ac￾tive appearance models and estimating the pose through a correlation model. The proposed technique is

coupled with adaptive principal component analysis (APCA), which was previously shown to perform

well in the presence of both lighting and expression variations. The proposed recognition techniques,

although advanced, are not computationally intensive. So they are quite well suited to the embedded sys￾tem environment. Indeed, the authors have implemented an early prototype of a face recognition module

on a mobile camera phone so the camera could be used to identify the person holding the phone.