Towards trainable man-machine interfaces : combining top-down constraints with bottom-up learning in facial analysis
Name
51641245-MIT.pdf
Description
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Author(s)
Kumar, Vinay P. (Vinay Prasanna), 1972-
Advisor(s)
Tomaso Poggio.
Alternative Title
Towards man-machine interfaces : combining top-down constraints with bottom-up learning
Date Issued
2002
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (Ph.D. in Computational Cognitive Science)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002.
Includes bibliographical references (leaves 72-[77]).
Subjects
Brain and Cognitive Sciences.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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