CVPR 2001 Short Course
Face Recognition by Humans & Machines:
A Tutorial Survey
Instructor:
Baback Moghaddam
(Mitsubishi Electric Research Laboratory)
Duration:
4 hours
COURSE
DESCRIPTION
The
purpose of this short course is to give the uninitiated as well as those just
starting in the field of face recognition, an historical overview of automatic
computerized face recognition as well as the physiological and cognitive
aspects of face recognition by primates and humans. This being CVPR, a greater
emphasis will be placed on computational techniques, drawing parallels with
neurophysiological findings, wherever appropriate. The ultimate goals of this
short course are to understand what’s so special about faces, just how hard is
face recognition by machines, and why is face recognition so important in the
coming age of ubiquitous cameras and unprecedented surveillance.
Specifically,
the course will:
*
highlight the special role of this important visual routine in the context of
sociological interaction and the ultimate survival of higher species dependent
on recognizing their individual members.
*
provide some insights based on recent findings as to how the primate brain has
specialized for this visual task. In particular, we’ll examine the role of
cortical areas IT and STS and the wealth of scientific knowledge gained by
single-cell recordings and cortical ablation in primates as well as the study
of performance deficits in humans where these "specialized areas" are
damaged by stroke and head trauma.
*
present a comprehensive survey of computer vision methods for face recognition
dating back from the 1960s to the current. The various methods covered will
include: feature-based, appearance-based, view-based, 2D shape-texture models,
3D shape-texture models, as well as various subspace representations (PCA, LDA,
ICA, etc.) and different matching algorithms (Lp k-NN, Bayesian, etc.)
*
present an overview of DARPA’s FERET program, its goals, testing methodologies
and competition results and why it has been so critical to recent progress in
the field
*
discuss the shortcomings of current state-of-the-art techniques and discuss
what can be done to overcome them. Some particular issues tackled are pose,
illumination, non-rigid deformation (expression) and the pros and cons of 3D
vs. view-based modeling.
BIOGRAPHY
Baback
Moghaddam is a Research Scientist at Mitsubishi Electric Research Laboratory
(MERL) working primarily in the areas of computational vision and image
processing. His current research interests are probabilistic visual modeling
for object recognition, facial analysis, statistical learning theory and
advanced pattern recognition techniques for biometrics. Prior to MERL, Dr.
Moghaddam was at the Vision & Modeling Group at the MIT Media Laboratory
where he developed the MIT automatic face recognition system that won the 1996
DARPA "FERET" face recognition competition. Dr. Moghaddam is a member
of the IEEE and ACM.
CONTACT
Baback
Moghaddam
Research
Scientist
Mitsubishi
Electric Research Laboratory
201
Broadway 8th floor
Cambridge,
MA 02139
Voice:
617-621-7524
Fax: 617-621-7550
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