Yanushkevich S.N., Wang P.S.P., Gavrilova M.L., Srihari S.N. (eds.) Image Pattern Recognition. Synthesis and Analysis in Biometrics
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April 2, 2007 14:42 World Scientific Review Volume - 9in x 6in Main˙WorldSc˙IPR˙SAB
418 Synthesis and Analysis in Biometrics
Harish Srinivasan is currently a Ph.D. candidate at the
department of computer science and engineering at the State
University of New York at Buffalo. His research is in:
(i) applications of statistical machine learning to biometric
verification using fingerprints, handwriting and signatures,
as the modalities, (ii) content-based image retrieval from
scanned handwritten documents and (iii) use of conditional
random fields in automatic segmenting and labelling of
scanned documents. His research contributions to the above fields have been
published at several conferences.
Harish Srinivasan received his B.Tech in Information Technology from the
Madras University in 2002 and an M.S. in Electrical Engeneering from the State
University of New York at Buffalo.
Adrian Stoica (Dipl Ing. TUI Iasi, Romania, PhD VUT
Melbourne, Australia) is a Principal in the Biologically
Inspired Technology and Systems Group in the Autonomous
Systems Division at the NASA Jet Propulsion Laboratory
(JPL), California Institute of Technology, Pasadena,
California. He leads JPL research in the areas of Evolvable
Systems, Humanoids Robotics and Integrated Circuits
Security. He has over 20 years of management of advanced
R&D and leadership in innovation and development of new technology in
electronics and information systems, robotics and automation, learning and
adaptive systems. His research interests and expertise are in the areas
of advanced electronics (extreme environment electronics, self-reconfigurable,
adaptive and evolvable hardware, adaptive computing devices, sensor fusion
hardware), secure electronics (obfuscation techniques for protection from reverse
engineering, trusted integrated circuits and biometrics protected ICs), biometrics
(embedded and multi-modal), artificial intelligence algorithms (automated circuit
design, search/optimization techniques, learning techniques, genetic algorithms,
neural networks, fuzzy systems), and robotics (robot learning, humanoid robots,
autonomous systems). He has over 100 papers and 4 patents in these areas, is
serving in the editorial board of several journals in the field, gave invited keynote
addresses and tutorials at conferences. He taught the first Evolvable Hardware
short course (UCLA Extension, 2003) and started two annual conferences: the
NASA/DOD Conference in Evolvable Hardware in 1999, and the NASA/ESA
Conference on Adaptive Hardware and Systems in 2006. He received the Lew
Allen Award for Excellence (highest NASA/JPL award for excellence in research)
in 1999, and the Tudor Tanasescu Prize of the Romanian Academy in 2001.
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About the Authors and Contributors 419
Issa Traor´e received an Aircraft Engineer degree from
Ecole de l’Air in Salon de Provence (France) in 1990,
and successively two Master degrees in Aeronautics
and Space Techniques in 1994, and in Automatics and
Computer Engineering in 1995 from Ecole Nationale
Superieure de l’Aeronautique et de l’Espace (E.N.S.A.E),
Toulouse, France. In 1998, Traor´e received a Ph.D.
in Software Engineering from Institute Nationale Poly-
technique (INPT)-LAAS/ CNRS, Toulouse, France. From June–Oct. 1998,
he held a post-doc position at LAAS-CNRS, Toulouse, France, and Research
Associate (Nov. 1998–May 1999), and Senior Lecturer (June-Oct. 1999) at the
University of Oslo. Since Nov. 1999, he has joined the faculty of the Department
of ECE, University of Victoria, Canada.
Dr. Traor´e, is currently and Associate Professor and holding since Oct. 2003
the position of Computer Engineering Programme Director. His research interests
include Behavioral biometrics systems, intrusion detection systems, software
security metrics, and software quality engineering.
Patrick S. W ang is IAPR Fellow, professor of Computer
Science at Northeastern University, research consultant at
MIT Sloan School, and adjunct faculty of computer science
at Harvard University Extension School. He received Ph.D.
in C.S. from Oregon State University, M.S. in I.C.S. from
Georgia Institute of Technology, M.S.E.E. from National
Taiwan University and B.S.E.E. from National Chiao Tung
University. He was on the faculty at University of Oregon
and Boston University, and senior researcher at Southern Bell, GTE Labs and
Wang Labs prior to his present position. Dr. Wang was elected Otto-Von-
Guericke Distinguished Guest Professor of Magdeburg University, Germany, and
serves as Honorary Advisor Professor for Xiamen University, China, and Guangxi
Normal University, Guilin, Guangxi, China. In addition to his research experience
at MIT AI Lab, Dr. Wang has been visiting professor and invited to give
lectures, do research and present papers in a number of countries from Europe,
Asia and many universities and industries in the U.S.A. and Canada, including
the recent iCore (Informatics Circle of Research Excellence) Visiting Professor
position at university of Calgary Dr. Wang has published over 120 technical
papers and 20 books in Pattern Recognition, A.I. and Imaging Technologies and
has 3 OCR patents by US and Europe Patent Bureaus. As IEEE Senior member,
April 2, 2007 14:42 World Scientific Review Volume - 9in x 6in Main˙WorldSc˙IPR˙SAB
420 Synthesis and Analysis in Biometrics
he has organized numerous international conferences and workshops including
conference co-chair of the 18th IAPR ICPR (International Conference on Pattern
Recognition) to be held in 2006, at Hong Kong, China, and served as reviewer
for many journals and NSF grant proposals. Prof. Wang is currently founding
Editor-in-Charge of Int. J. of Pattern Recognition and A.I., and Editor-in-Chief
of Machine Perception and Artificial Intelligence by World Scientific Publishing
Co. and elected chair of IAPR-SSPR (Int. Assos. for P.R.). In addition to his
technical achievements and contributions, Prof. Wang has been also very active
in community service, and has written several articles on Du Fu, Li Bai’s poems,
Verdi, Puccini, Bizet, and Wagner’s operas, and Mozart, Beethoven, Schubert
and Tchaikovsky’s symphonies.
Svetlana N. Yanushkevich received M.S. (1989) and
PhD (1992) degrees in electrical and computer engineering
from the State University of Informatics and Radioelect-
ronics, Minsk, Belarus, and she received her Habilitation
degree (1999) in engineering science from the University of
Technology, Warsaw, Poland. She joined the Department of
Electrical and Computer Engineering, University of Calgary,
Canada, in 2001. She is the founding director of the
“Biometric Technologies: Modeling and Simulation” Laboratory at Calgary.
Dr. Yanushkevich is a Senior Member of the IEEE, a Member of IAPR,
and a Member of the Institute of Electronics, Information and Communication
Engineers (IEICE), Japan.
Dr. Yanushkevich took part in several industrial projects on the development
of (i) high-performance parallel computing tools for image processing applications,
including document understanding, (ii) systems for forensic expertise support
based on behavioral biometrics, and (iii) bank staff training systems for signature
and handwriting verification. Her current research interests include problems in
direct biometrics (analysis) and inverse biometrics (synthesis), computer aided
design of systems, nanocomputing in 3D, and artificial intelligence in decision
making. She also acts as a consultant to several government and commercial
institutions.
Dr. Yanushkevich is the Chair of the IEEE-Industrial Electronics Society
Technical Subcommittee for Biometric Technologies, and a Member of the IEEE
Technical Committee on Multiple-Valued Logic. She serves as a Guest Editor of
several international journals, and she was a general chair and co-chair of about
20 international conferences, symposia, and workshops.
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About the Authors and Contributors 421
Dr.Yanushkevich published over 200 technical papers, including 15 patents,
and 8 authored, co-authored, and 3 edited books. She co-authored (with
D. M. Miller, V. P. Shmerko, and R. S. Stankovi´c) the first handbook on
logic design of nanocircuits, “Decision Diagram Techniques for Micro- and
Nanoelectronic Design, Taylor&Francis, CRC Press, 2006. The book “Biometric
Inverse Problems”, Taylor&Francis, CRC Press, 2005, written together with A.
Stoica, V. Shmerko, and D. Popel, is the first book that focuses on the generation
of synthetic biometric data.
Xinge You received the B. S. and M. S. degrees in
mathematics from the University of Hubei, Wuhan, China
and the Ph.D. degree in computer science from the Hong
Kong Baptist University, Hong Kong, in 1990, 2000, and
2004 respectively. He is presently an Associate Professor
in the Faculty of Mathematics Computer Science at Hubei
University, China. And he is currently work as postdoctoral
fellow in the Department of Computer Science at Hong
Kong Baptist University, Hong Kong. His current research interests include
wavelets and its application, signal and image processing, pattern recognition,
and computer vision.
Dan Zhang received the BSc degree in mathematics
from Huanggang Normal College, Hubei, China, in 2004.
Currently she is pursuing her master degree in the
Faculty of Mathematics and Computer science, Hubei
University, China. Her research interests include image
processing, pattern recognition and biometrics technologies
and applications. pattern recognition and biometrics
technologies and applications.
David Zhang graduated in Computer Science from
Peking University in 1974. In 1983 he received his MSc
in Computer Science and Engineering from the Harbin
Institute of Technology (HIT) and then in 1985 his PhD
from the same institution. From 1986 to 1988 he was first
a Postdoctoral Fellow at Tsinghua University and then
an Associate Professor at the Academia Sinica, Beijing.
In 1994 he received his second PhD in Electrical and
Computer Engineering from the University of Waterloo, Ontario, Canada.
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422 Synthesis and Analysis in Biometrics
Currently, he is a Chair Professor, the Hong Kong Polytechnic University where
he is the Founding Director of the Biometrics Technology Centre. Professor
Zhang’s research interests include automated biometrics-based authentication,
pattern recognition, and biometric technology and systems. He is the Founder
and Editor-in-Chief, International Journal of Image and Graphics; Book
Editor, Kluwer International Series on Biometrics; Chairman, Hong Kong
Biometric Authentication Society and Program Chair, the First International
Conference on Biometrics Authentication (ICBA), Associate Editor of more than
ten international journals including IEEE Trans on SMC-A/SMC-C, Pattern
Recognition, and is the author of more than 140 journal papers, twenty book
chapters and eleven books. Professor Zhang holds a number of patents in both
the USA and China and is a Croucher Senior Research Fellow and Distinguished
Speaker of IEEE Computer Society.
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Author Index
Ahmed, A. A. E., 243, 407
Bartels, R. H., 65, 407
Bolle, R. M., 269
Chen, Q., 207
Coello Coello, C. A., 289, 408
Elliott, S. J., 339, 408
Gavrilova, M. L., 103, 409
Hurley, D. J., 183, 409
Kong, W.-K., 225, 410
Kukula, E. P., 339, 410
Liao, Q., 225, 411
Lu, G., 225, 411
Modi, S. K., 339, 412
Nixon, M., 412
Olsen, L., 65, 413
Pankanti, S., 269
Parker, J. R., 159, 413
Phillips, P., 414
Popel, D. V., 31, 414
Ratha, N. K., 269, 415
Rogers, B., 415
Samavati,F.F.,416
Samavati,F.F.,65
Shmerko, V. P., 5, 365
Srihari, S., 5, 139, 417
Srinivasan, H., 139, 418
Stoica, A., 5, 365, 418
Traor´e, I., 243, 419
Wang, P. S. P., 5, 207, 321, 419
Yanushkevich, S. N., 5, 365
You, X., 207, 421
Zhang, Dan, 207, 421
Zhang, David, 225, 421
423
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Subject Index
A
Acceptability, 23, 173, 341, 376
Access, 104, 271, 390
control system, 25, 365, 373
Accuracy, 55, 245, 280
Acoustic
band, 380
domain, 377
Algorithm
evolutionary, 290
genetic, 290
Nearest feature line, 208
NearestNeighbor, 208
Alignment, 37, 230
American National Standards Institute
(ANSI), 348
Analysis, 7
kernel Fisher’s discriminant, 208,
209
linear discriminant (LDA), 208,
209
multiresolution, 208, 211
Analysis-by-synthesis, 7, 25
Animation, 9, 121, 323
Appearance, 10, 369
Approximation, 126
matrix, 254
Arch, 276, 284
Architecture, 32, 55, 246
Artificial
data, 6, 7
immune system, 289, 313
intelligence, 22, 33, 243, 261,
290
Assembling, 48
Authorization, 365
Authorized
individual, 389
user, 11, 104, 245
Automated
fingerprint identification system
(AFIS), 284
methods, 13, 207, 322
recognition, 340
system, 60, 367
B
B-spline, 65
cubic, 66, 74, 75
filter, 80
quadratic, 75, 76, 80
wavelet, 69, 89
Bayesian
approach, 386
belief network (BBN), 398
statistics, 386, 387
Bezier curve, 12, 14
Binning, 276, 279
Biometric
behavioural, 32, 244, 340, 396
data, 245
hiding, 323
device, 273, 344, 369
encryption, 323
identification, 271
modalities, 5, 340
425
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426 Synthesis and Analysis in Biometrics
physiological, 106, 244, 340
synthetic, 6, 7, 370, 401
system, 21, 107, 245, 339
verification, 139, 140, 272
Blood
flow rate, 369, 378
pressure, 376, 398
Borda count, 160, 174
C
Cancelable biometrics, 22, 323
Caricature, 19
Classification, 106, 165, 171, 208
Classifier, 160, 166, 220, 235, 310
Computational geometry, 108
Computer aided
design, 17
training, 384
Confusion matrix, 165
Convergence map, 198, 199
Cooperation
human-human, 379
human-machine, 380, 381
machine-machine, 380
Crossover, 291
single-point, 299
two-point, 299
uniform, 299, 300
D
Data
acquisition, 7, 56
representation, 114
Database, 104, 271
centralized, 273
distributed, 273
Decision
diagram, 397
fusion, 235
making, 282, 309, 368
support, 22, 370, 382, 394
threshold, 281
Decomposition, 67
coefficients, 234
filter, 68
Delaunay
tessellation, 111
triangulation, 107, 109
Digram, 51
model, 52
Discriminant
function, 236
Discriminating
element, 139–141
Dissenting-weighted majority vote
(DWMV), 160, 172
Distance
distribution, 109, 118
Euclidean, 110, 119
transform, 117, 118, 120, 162
vector, 236
Distribution
Gamma, 146, 148
Gaussian, 202
Divergence, 183, 197
Domain
frequency, 187, 228
knowledge, 374, 376
signal, 400
E
Ear
biometrics, 183
signature, 204
topology, 186
Early warning, 390
paradigm, 367
Eigenfaces, 209
Electromagnetic spectrum, 377
Emotion, 9, 321
Energy
distribution, 228
field, 183, 190
transform, 190, 192
Enrollment, 244, 271
Environment
real-world, 371
virtual, 392, 402
Equal error rate (EER), 140, 151
Ergonomics, 343, 373
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Subject Index 427
Error
criterion, 249
rate, 140, 152, 276
Ethical
aspects, 24
issues, 373
Euclidian
metric, 110, 111
Voronoi diagram, 110
Evaluation
empirical, 178
performance, 219, 279, 324
Evolutionary
programming, 293
Expert system, 383
F
Face
recognition, 9, 207, 208, 339,
342, 346, 374
vendor test (FRVT), 282, 340,
341
recognition, 324
reconstruction, 8, 9, 17
synthesis, 8, 17, 402
Facial expression, 9
modeling, 103, 109, 121
morphing, 118
Factor
environmental, 346
human, 367, 385
Failure
to acquire (FTA), 270, 276, 357
to capture (FTC), 244, 245
to enroll (FTE), 244, 245, 270,
276
False
accept rate (FAR), 270, 279
reject rate (FRR), 270, 279
Feature
extraction, 89
vector, 168, 235, 309
Feedback, 93, 375
Filter
Gabor, 11
low-pass, 216
orthogonal, 215
Fingerprint
recognition, 283
synthesis, 10
verification, 149
Flexibility, 371, 390
Force field, 183, 185
feature extraction, 185, 192
transformation, 185
Forensic science, 184
Forger, 7, 33
Fourier transform, 14, 190, 231
Free text detection, 254
G
Gait
biometrics, 379
modeling, 16
recognition, 16
signature, 16
Gaussian
kernel, 198
Gyropen, 56
H
Hand geometry
identification, 9
reader, 345
Handwriting, 34, 139
character recognition, 9
imitation, 12
Histogram
frequency, 166
slope, 166
Human biometric sensor interaction
(HBSI)) 339, 341, 344
Humanoid robot, 21
I
Identification, 271
number, 270
system, 117, 270, 272
Illumination
condition, 347