Face recognition : methods, applications and technology

252 Pages·2012·18.589 MB·English

by  Adamo Quaglia, Calogera M Epifano

COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS

F R
ACE ECOGNITION

METHODS, APPLICATIONS AND TECHNOLOGY

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COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS

F R
ACE ECOGNITION

METHODS, APPLICATIONS AND TECHNOLOGY

ADAMO QUAGLIA
AND
CALOGERA M. EPIFANO
EDITORS

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Library of Congress Cataloging-in-Publication Data

Face recognition : methods, applications, and technology / editors, Adamo Quaglia and Calogera
M. Epifano.
p. cm.
Includes bibliographical references and index.
ISBN: (cid:28)(cid:26)(cid:27)(cid:16)(cid:20)(cid:16)(cid:25)(cid:20)(cid:20)(cid:21)(cid:21)(cid:16)(cid:25)(cid:21)(cid:24)(cid:16)(cid:25) (eBook)
1. Human face recognition (Computer science) I. Quaglia, Adamo. II. Epifano, Calogera M.
TA1650.F34 2012
006.3'7--dc23
2011051149

 New York
CONTENTS

Preface vii 
Chapter 1 Accuracy of Face Recognition 1 
Ahmed M. Megreya 
Chapter 2 Extended 2-D PCA for Face Recognition:
Analysis, Algorithms, and Performance Enhancement 29 
Ana-Maria Sevcenco and Wu-Sheng Lu 
Chapter 3 Face Recognition Based on Composite Correlation Filters:
Analysis of Their Performances 57 
I. Leonard, A. Alfalou and C. Brosseau 
Chapter 4 Face Recognition Employing PCA Based
Artificial Immune Networks 81 
Guan-Chu Luh 
Chapter 5 Distributed Face Recognition 105 
Angshul Majumdar, Rabab K. Ward
and Panos Nasiopoulos 
Chapter 6 Facial Identity, Facial Emotion Recognition and Cognition
in Remitted vs. Non-Remitted Patients With Schizophrenia 123 
Filomena Castagna, Cristiana Montemagni, 
Monica Sigaudo, Cinzia Mingrone and Paola Rocca 
Chapter 7 Face Recognition: Different Encoding Methods
on Newborn Infant Research 137 
Cecchini Marco, Iannoni Maria Elena,
Di Florio Eugenio, Altavilla Daniela,
Piccolo Federica, Aceto Paola and Lai Carlo 
Chapter 8 Multi-Class Learning Facial Age Estimation
with Fused Gabor and LBP Features 151 
Jian-Gang Wang
vi Contents
Chapter 9 Techniques of Frequency Domain Correlation
for Face Recognition and Its Photonic Implementation 165 
Pradipta K. Banerjee and Asit K. Datta 
Chapter 10 Forensic Face Recognition: A Survey 187 
Tauseef Ali, Luuk Spreeuwers
and Raymond Veldhuis 
Chapter 11 Correlation and Independent Component Analysis
Based Approaches for Biometric Recognition 201 
P. Katz, A. Alfalou, C. Brosseau and M. S. Alam 
Index 231
PREFACE

Face recognition has been an active area of research in image processing and computer
vision due to its extensive range of prospective applications relating to biometrics,
information security, video surveillance, law enforcement, identity authentication, smart
cards, and access control systems. Topics discussed in this compilation include two-
dimensional principal component analysis algorithms for face recognition; principal
component analysis (PCA) and artificial immune networks in face recognition; multi-class
learning facial age estimation and forensic face recognition.
Chapter 1 – Although we have an excellent ability to recognise familiar faces even under
challenging viewing conditions (e.g., low-quality and old images), people are remarkably
poor at identifying unfamiliar faces even under optimal circumstances (e.g., high-quality and
recent images). This chapter discusses unfamiliar face identification accuracy and reports
convergent evidence from several research fields including face recognition, eye-witness
identification, and change blindness. In addition, recent face perception experiments suggest
that the limitatons of unfamiliar face recognition might be arised during encoding faces in the
first place, rather than processing them into memory. Importantly, however, there are quite
large individual differences in unfamiliar face perception, but only few studies tried to predict
these variabilities.
Chapter 2 – Two-dimensional (2-D) principal component analysis (PCA) algorithms for
face recognition are an attractive alternative to the traditional one-dimensional (1-D) PCA
algorithms because they are able to provide higher accuracy in extracting facial features for
human face identification with reduced computational complexity. The improved
performance and efficiency are gained largely because the 2-D PCA algorithms treat facial
images more naturally and effectively as matrices rather than vectors and, as a result, one can
employ a covariance matrix of much reduced-size for analysis and algorithmic development.
This chapter presents an extended 2-D PCA algorithm for improving rate of face recognition.
The new algorithm is deduced based on an analysis in which a pair of (rather than a single)
covariance matrices are defined and utilized for extracting both row-related and column-
related features of facial images. In addition, by incorporating a pre-processing procedure,
which was recently developed by the authors based on perfect histogram matching, the
performance of the proposed 2-D PCA algorithm can be considerably enhanced.
Experimental results are presented to examine the performance of the proposed algorithms
and their robustness to noise, face occlusion, and illumination conditions.
Chapter 3 – This chapter complements our paper: ”Spectral optimized asymmetric
segmented phase-only correlation filter ASPOF filter” published in Applied Optics (2012).
viii Adamo Quaglia and Calogera M. Epifano
Chapter 4 – This study proposes a face recognition method based on Principal
Component Analysis (PCA) and artificial immune networks. The PCA extracts principal
eigenvectors of the face image to get best feature description, consequently to reduce the size
of feature vectors of the artificial immune networks. Hereafter these reduced-dimension
image data are input into immune network classifiers to be trained. Subsequently the
antibodies of the artificial immune networks are optimized using genetic algorithms. The
performance of the proposed method was evaluated employing the ORL (ATandT
Laboratories Cambridge) face database. The results show that this method gains higher
recognition rate in contrast with most of the developed methods even for single training
sample problem.
Chapter 5 – This work addresses the problem of distributed face recognition. In this
problem, the training data does not reside on a single computer; it resides in multiple
computers which are distributed geographically. The challenge is to develop a face
recognition system where the dimensionality reduction and classification modules have
access to only a small portion (few classes) of the entire training data. Such problems will
arise when face recognition are employed at a large scale such as automatic client
authentication in bank ATMs or automatic employee authentication in offices. Popular
dimensionality reduction methods such as Principal Component Analysis (PCA), Linear
Discriminant Analysis (LDA) etc. are data-dependent and so are classification algorithms like
Neural Networks and Support Vector Machines. Therefore these methods are not suitable for
this problem. This paper proposes a novel solution of the problem based on recognizing faces
from video sequences.
For dimensionality reduction, we employ random projections as a data-independent
alternative to PCA or LDA. The classification is carried out in parallel by the Hidden Markov
Model (HMM) and our newly proposed Nearest Subspace Classifier (NSC). The classifiers
are designed in such a way that they can be applied to each class separately; therefore they
can operate on the smaller portions (few classes) of the data that reside on individual
computers. The results from the two classifiers are finally fused to arrive at the final
classification decision.
We have identified a new problem in face recognition. Therefore there are no previous
studies that can address this problem. However, in order to see how our proposed method
works with previous ones we have compared our results with a few previous works in video
based face recognition. Our method shows better results than the ones we have compared
with.
Chapter 6 – Aim. A growing interest has been directed to evaluate whether a symptom-
based remitted state, based on standardized criteria for schizophrenia, also corresponds to an
overall good functioning. This study aimed to examine the relationships between remission
status and two outcome parameters in a group of schizophrenic patients: face processing
(facial identity and facial emotion recognition) and basic cognitive abilities. Methods. Ninety
patients in stable phase were enrolled, of whom 28 patients attained “cross-sectional”
remission, and 62 patients failed to. Facial emotion perception performances were assessed
with the Comprehensive Affect Testing System, a standardized measure of emotion
processing. Cognitive functions were evaluated by a wide battery of tests for attention, verbal
memory/learning, perceptual-motor speed and executive functions. Results. The two groups
of patients were homogeneous in demographic and clinical variables (age of onset, length of
illness, previous relapses, dose equivalent to 100 mg/day of chlorpromazine, type of