A Sparse Representation Perspective on Face Recognition
Speaker: Yi Ma , UIUCContact:
Date: April 25 2007
Time: 3:00PM to 4:00PM
Location: Star Seminar Room (32-D463)
Host: C. Mario Christoudias, Gerald Dalley, MIT CSAIL
C. Mario Christoudias, Gerald Dalley, 3-4278, 3-6095, email@example.com, firstname.lastname@example.orgRelevant URL:
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 over-looked, which holds the key
for high-performance 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 down-sampled
or occluded images.
This is joint work with John Wright at UIUC and Allen Yang at UC Berkeley.
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.
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