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. 


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. 


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