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Information
maximization, natural image statistics, and face processing |
This work explores principles of unsupervised
learning and adaptation to natural image statistics and how they may relate to
face processing. Dependency coding and information maximization appear to be
central principles in neural coding early in the visual system. These
principles may be relevant to how we think about higher visual processes such
as face recognition as well. The work explores studies of human face perception
from an information maximization perspective, and presents an information
maximization account of perceptual effects such as the atypicality
bias, and face adaptation aftereffects.
Long F, Wu T, Movellan J, Bartlett M, Littlewort, G (2012).
Learning spatiotemporal features by using independent component analysis
with application to facial expression recognition. Neurocomputing
93: 126-132 (2012). Download pdf
Tanaka JW, Kantner J and Bartlett
M (2012) How category structure influences the
perception of object similarity: The atypicality
bias. Front. Psychology 3:147. Download pdf
Bartlett, M.S. (2009). Information
maximization in face processing. Invited talk,
Principles of Autonomous Neurodynamics, La
Jolla, CA, July 27-29, (2009). Abstract pdf
Bartlett,
M.S. (2007). Information maximization in face processing.
Neurocomputing 70, p. 2204-2217.
Download pdf
Susskind,
J.M., Littlewort, G.C., Bartlett, M.S., Movellan, J.R., and Anderson, A.K. (2007). Human and computer recognition of facial expressions of emotion.
Neuropsychologia 45(1), p. 152-162. Download pdf
Bartlett,
M.S., Movellan J.R. & Sejnowski,
T.J. (2006). Face modeling by information maximization.
In R. Chellappa and Y. Zhao, Eds. Face Processing:
Advanced Modeling and Methods. Elsevier, p. 219-253. Reprinted with permission
from Elsevier.
Download pdf
Bosworth, R.G.,
Bartlett, M. S., and Dobkins, K. R. (2006). Image
Statistics of American Sign Language: Comparison to Faces and Natural Scenes. Journal of the Optical Society of America A 23(9) p. 2085-2096.
Bartlett, Marian S.
(2004). Information maximization in face processing. Poster presentation, Proceedings of the 2nd International
Conference on Development and Learning.
Bosworth, R. G.,
Wright, C. E., Bartlett, M. S., Corina, D. P., and Dobkins, K. R. Characterization of visual properties of
spatial frequency and speed in American Sign Language (2003). In A. E. Baker,
B. van den Bogaerde, & O. Crasborn
(Eds), Cross-Linguistic Perspectives in Sign Language
Research: Selected Papers from TISLR 2000. Hamburg: Signum
Press.
Abstract, Download pdf
Bartlett,
M.S. (2003). Unsupervised learning in face recognition.
Invited talk, About Faces: A multidisciplinary Approach to the Science of Face
Perception. Princeton University, Princeton, NJ, September 19-21.
Abstract
Bartlett,
M. S., (2001). Face Image Analysis by Unsupervised Learning. Foreword
by Terrence J. Sejnowski. Kluwer
International Series on Engineering and Computer Science, V. 612.
Boston: Kluwer Academic Publishers. Summary
Bartlett, M.S. and Tanaka, J.W. (1998). An attractor field
model of face representations: Effects of typicality and image morphing. Psychonomics Society Satellite Symposium on Object
Perception and Memory (OPAM), Dallas, TX, November 19.
Abstract
Bartlett, M.S., and Sejnowski,
T.J. (1998). Learning viewpoint invariant face representations from visual
experience in an attractor network. Network: Computation in Neural Systems
9(3) 399-417.
Abstract, Download ps.gz
Bartlett,
M. Stewart, and Sejnowski, T.J. (1998). Learning
Viewpoint Invariant Face Representations from Visual Experience by Temporal
Association. In H. Wechsler, P.J. Phillips, V. Bruce, S. Fogelman-Soulie, T. Huang (Eds.), Face
Recognition: From Theory to Applications, NATO ASI Series F.Springer-Verlag.
Abstract, Download ps