Marian Stewart Bartlett
Marian Stewart Bartlett, Ph.D.
Institute for Neural Computation
University of California, San Diego, 0523
9500 Gilman Drive
La Jolla, CA 92093-0523
Phone: (858) 822-5241
Fax: (858) 534-2014
I am Assistant Research Professor at the Machine Perception Lab
at the Institute for Neural Computation, UCSD. I received my PhD in Cognitive Science and Psychology from the
University of California, San Diego in 1998. I did my Ph.D research with Terrance
Sejnowski at the Computational Neurobiology Laboratory at the Salk
Institute. My doctoral dissertation was on "Face image analysis by
unsupervised learning and redundancy reduction." View Abstract. My Postdoc was with Javier Movellan at the Institute for Neural Computation, UCSD.
My research interests include
Image analysis through unsupervised learning.
Facial identity recognition.
Facial expression analysis.
Independent component analysis for pattern recognition.
matlab code for face representations using ICA. REVISED 7/2003. FULL MATLAB CODE FOR ARCHITECTURES I AND II NOW AVAILABLE.
companion code for running face recognition using ica. This code includes nearest neighbor using cosines.
Here are photos of my son, Paul 1 2 3 and daughter, Kate 1 2.
Face Image Analysis
by Unsupervised Learning
Marian Stewart Bartlett Kluwer International Series on Engineering and
Computer Science, V. 612. Boston: Kluwer
Academic Publishers, 2001.
(888)-640-7378. Also available on Amazon.com.
Face Image Analysis by Unsupervised Learning explores adaptive approaches
to face image analysis. It draws upon principles of unsupervised learning
and information theory to adapt processing to the immediate task
environment. In contrast to more traditional approaches to image analysis
in which relevant structure is determined in advance and extracted using
hand-engineered techniques, [this book] explores methods that have roots in
biological vision and/or learn about the image structure directly from the
image ensemble. Particular attention is paid to unsupervised learning
techniques for encoding the statistical dependencies in the image ensemble.
The first part of this volume reviews unsupervised learning, information
theory, independent component analysis, and their relation to biological
vision. Next, a face image representation using independent component
analysis (ICA) is developed, which is an unsupervised learning technique
based on optimal information transfer between neurons. The ICA
representation is compared to a number of other face representations
including eigenfaces and Gabor wavelets on tasks of identity recognition
and expression analysis. Finally, methods for learning features that are
robust to changes in viewpoint and lighting are presented. These studies
provide evidence that encoding input dependencies through unsupervised
learning is an effective strategy for face recognition.
Face Image Analysis by Unsupervised Learning is suitable as a secondary
text for a graduate level course, and as a reference for researchers and
practioners in industry.
"Marian Bartlett's comparison of ICA with other algorithms on the
recognition of facial expressions is perhaps the most thorough analysis we
have of the strengths and limits of ICA as a preprocessing stage for
- T.J. Sejnowski, The
May 7, 2001: