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Towards Man-Machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis

Author(s)
Kumar, Vinay P.
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Abstract
This thesis proposes a methodology 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.
Date issued
2002-09-01
URI
http://hdl.handle.net/1721.1/5569
Other identifiers
AITR-2002-008
CBCL-221
Series/Report no.
AITR-2002-008CBCL-221
Keywords
AI, Facial Expression Recognition, Pose Estimation, Viseme Recognition, SVM

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