Visual Feature Extraction by Unified Discriminative Subspace Learning

cobblerbeggarAI and Robotics

Oct 15, 2013 (3 years and 8 months ago)

92 views

Visual Feature Extraction by
Unified
D
iscriminative Subspace Learning


Yun (
Raymond
)

Fu

Dr. and Scientist

BBN Technologies

10 Moulton Street

Cambridge, MA 02138

http://www.ifp.uiuc.edu/~yunfu2/




Abstract

Multimodality visual data analysis through discriminative subspace learning

was an
extensively discussed topic over the past several decades. It attracts much attention
from interdisciplinary fields
recently
due to

the increasing demand for developing

real
-
world

human
-
computer

interaction
and computer vision
systems. A large family of
subspace learning methods has been designed
in the pattern recognition field
based on
different
motivations and
objective functions. Although they are diversified, it is
intui
tive

to
uncover

some common ideas
from

them. Can we unify them and formulate
new algorithms
to

further

enhance the discriminat
ing

power

for

feature extraction
?
Stemmed from this motivation, a
unified

framework of discriminative subspace
learning
will be

pr
esented

in this talk
.


Based on the proposed
general
framework, several new subspace learning algorithms
are designed.
Those
methods

are

successfully applied to
real
-
world

applications

of
face
biometrics
, such as face recognition, head pose estimation,
re
alistic
expression/emotion analysis, human age estimation, and lipreading.


Short
Bio


Dr. Yun
(
Raymond
)

Fu received the B.Eng. degree in information engineering from the
School of Electronic and Information Engineering, Xi'an Jiaotong University (XJTU),
China, in 2001; the M.Eng. degree in pattern recognition and intelligence systems from
the Artificial Intelligence and Robotics Institute (AI&R), XJTU, in 2004; the M.S. degree
in statistics from the Department of Statistics, University of Illinois at
Urba
na
-
Champaign (UIUC), USA, in 2007; and the Ph.D degree in Electrical and
Computer Engineering (ECE) from ECE Department, UIUC, USA, in 2008.

From 2001 to 2004, he was a research assistant at the AI&R at XJTU. From 2004 to
now
, he wa
s a graduate fellow and
research assistant at the Beckman Institute for
Advanced Science and Technology, ECE department and Coordinated Science
Laboratory at UIUC. He was a research intern with Mitsubishi Electric Research
Laboratories, Cambridge, MA, in summer 2005; with Multime
dia Research Lab of
Motorola Labs, Schaumburg, IL, in summer 2006. He jointed BBN Technologies,
Cambridge, MA, as a Scientist in 2008 to build and lead the computer vision and
machine learning team.

He did interdisciplinary work and his research interests
include statistical machine
learning, human computer interaction, image processing, multimedia and computer
vision. He has extensive publications in top journals, book chapters and international
conferences/workshops. He serves as associate editor, chairs,

PC member and
reviewer of top journals and international conferences/workshops. He is the recipient
of the 2002 Rockwell Automation Master of Science Award, two Edison Cups of the
2002 GE Fund Edison Cup Technology Innovation Competition, the 2003
Hewlett
-
Packard (HP) Silver Medal and Science Scholarship, the 2007 Chinese
Government Award for Outstanding Self
-
financed Students Abroad, the 2007 DoCoMo
USA Labs Innovative Paper Award (IEEE ICIP'07 best paper award), the 2007
-
2008
Beckman Graduate Fellowship
of Arnold and Mabel Beckman Foundation, and the 2008
M. E. Van Valkenburg Graduate Research Award. He was one of the finalists of the 2008
Lemelson
-
Illinois Student Prize and 2007
-
2008 Illinois International Graduate
Achievement Award.


He is a member of
IEEE, life member of Institute of Mathematical Statistics (IMS), and
2007
-
2008 Beckman Graduate Fellow.