Systems Seminar
A Sparse Representation Perspective on Face Recognition
Prof. Yi Ma
ECE Dept.
Univ. of Illinois at Urbana-Champaign
Abstract
Image-based object recognition is one of the quintessential
problems for computer vision, and human faces are arguably the
most important class of objects to recognize. Despite extensive
studies and practices on face recognition in the past couple of
decades, we in this talk contend that a critical piece of
information has largely been overlooked, which holds the key
for high-performance robust face recognition.
That is, to a large extent, object recognition, and particularly
face recognition under varying illumination, can be cast as a
sparse representation problem. Based on L1-minimization, we
propose an extremely simple but effective algorithm for face
recognition that significantly advances the state-of-the-art.
Within this unified computational framework, we systematically
address two fundamental issues in face recognition: the role of
feature selection and the issue with occlusion.
Some of the new results and findings can be rather surprising,
and even go against the conventional wisdom. For example, we
will show that once sparsity is properly harnessed, the choice
of features is no longer critical for recognition. Severely down
sampled or randomly projected face images perform almost equally
well as conventional features such as Eigenfaces and
Laplacianfaces. Furthermore, the performance of such a simple
algorithm arguably surpasses the capabilities of human
recognizing severely downsampled or occluded images.
This is joint work with John Wright at UIUC and Allen Yang at UC
Berkeley.
Biography:
Yi Ma is an associate professor at the Electrical & Computer
Engineering Department of the University of Illinois at Urbana-Champaign.
His research interests include computer vision and
systems theory. Yi Ma received two Bachelors? degree in
Automation and Applied Mathematics from Tsinghua University
(Beijing, China) in 1995, a Master of Science degree in EECS in
1997, a Master of Arts degree in Mathematics in 2000, and a PhD
degree in EECS in 2000, all from the University of California at
Berkeley. Yi Ma received the David Marr Best Paper Prize at the
International Conference on Computer Vision 1999 and the
Longuet-Higgins Best Paper Prize at the European Conference on Computer
Vision 2004. He also received the CAREER Award from the National
Science Foundation in 2004 and the Young Investigator Award from
the Office of Naval Research in 2005. He is an associate editor
of IEEE Transactions on Pattern Analysis and Machine
Intelligence. He is a senior member of IEEE and a member of ACM.
Time and Place: Wed., Feb. 27, at 3:30 pm in 4610.
SYSTEMS SEMINAR WEB PAGE:
http://homepages.cae.wisc.edu/~gubner/seminar/schedule.html