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Towards man-machine interfaces : combining top-down constraints with bottom-up learning

By 1972- Vinay P. (Vinay Prasanna) Kumar

Abstract

This thesis proposes a miethodology for the design of man-machine interfaces by combining top-down and bottom-up processes in vision. From a computational perspective, we propose that the scientific-cognitive question of combining top-down and bottom-up knowledge is similar to the engineering question of labeling a training set in a supervised learning problem. We investigate these questions in the realm of facial analysis. We propose the use of a linear morphable model (LMM) for representing top-down structure and use it to model various facial variations such as mouth shapes and expression, the pose of faces and visual speech (visemes). We apply a supervised learning method based on support vector machine (SVM) regression for estimating the parameters of LMMs directly from pixel-based representations of faces. We combine these methods for designing new, more self-contained systems for recognizing facial expressions, estimating facial pose and for recognizing visemes.by Vinay P. Kumar.Thesis (Ph.D. in Computational Cognitive Science)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002.Includes bibliographical references (leaves 72-[77])

Topics: Brain and Cognitive Sciences.
Publisher: Massachusetts Institute of Technology
Year: 2002
OAI identifier: oai:dspace.mit.edu:1721.1/29243
Provided by: DSpace@MIT
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