long_title : Face Recognition: From Theory to Applicatons
short_title : Face Recognition
url :
http://www.springer.de/cgi-bin/search_book.pl?link=/jour/svcat/deutsch2/comp/3540644105.html&isbn=3-540-64410-5
FACE RECOGNITION: FROM THEORY TO APPLICATIONS
Editors : H. Wechsler, J.P. Phillips, V. Bruce, F. Fogelman Soulie and
T.
Huang
estimated price : $129
springer contact : Woessner@springer.de
abstract from the book :
An NATO Advanced Study Institute (ASI) on Face Recognition was
held in Stirling, UK, in the summer of 1997. Face Recognition (FR), a
complex and difficult problem, is important for surveillance and
security, telecommunications and digital libraries, human-computer
intelligent interactions, and medicine. FR solutions presented have
been shown to require synergetic efforts from fields such as signal
and image processing, pattern recognition, machine learning, neural
networks and evolutionary computation, psychophysics of human
perception and neurosciences, and system engineering. The ASI
brought together leading researchers from academia, government,
and industry from around the world to present an all encompassing
view on FR, and identify trends for future developments and the
means for implementing robust FR systems.
Most of the FR methods presented implement some variation on
either the Principal Component Analysis (PCA) approach, also known
as the eigenfaces approach, or the Dynamic Link Architectures (DLA),
where elastic graph matching is attempted between locally derived
forensic landmark grids, possibly encoded using Gabor wavelets. The
PCA method records 2nd order statistics across the face and can be
enhanced using spatiotemporal constraints encoded as manifolds
('trajectories') corresponding to the views obtained as the face
rotates in 3D space. Further enhancements on the basic FR methods
considers (i) active vision and (ii) modular forensic systems
consisting of similar or different modalities, as it would be the case
when human authentication or surveillance is done by fusing visual
and audio information.
The meeting took an active interest in both cognitive sciences and
neurosciences with the goal to determine what is most important for
face recognition. As an example, color information should help with
face detection, while sequential classifiers should perform better
than flat classifiers on face recognition. Another concept discussed
was that of using consensus networks or multiple decision trees for
handling the inherent variability of the image formation process and
the uncertainty involved in modeling the overall FR system. The
discussion held also emphasized the ever increasing role video
processing will play in face recognition as additional frames of facial
imagery and motion information contribute to increased confidence
in face recognition.
An important topic regarding FR is the development of appropriate
means for performance evaluation. Towards that end it has become
clear that one has to develop standard data bases of faces images,
such as FERET, in order to assess and compare between competing FR
systems. Decision theory and ROC curves provide the tools needed to
quantify the level of performance displayed by specific FR systems.
Statistical learning theory further provides the means to estimate the
guaranteed risk, when testing on future and unseen facial imagery,
in terms of the empirical risk experienced during training and the
complexity of the classification model underlying the FR system.