Towards Man-Machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis
Item
-
Title
-
en_US
Towards Man-Machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis
-
Creator
-
en_US
Kumar, Vinay P.
-
Date
-
2004-10-01T14:00:07Z
-
Date Available
-
2004-10-01T14:00:07Z
-
Date Issued
-
en_US
2002-09-01
-
Identifier
-
en_US
AITR-2002-008
-
en_US
CBCL-221
-
Abstract
-
en_US
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.
-
Extent
-
en_US
68 p.
-
21293042 bytes
-
2473001 bytes
-
Format
-
application/postscript
-
application/pdf
-
Language
-
en_US
-
Relation
-
en_US
AITR-2002-008
-
en_US
CBCL-221
-
Subject
-
en_US
AI
-
en_US
Facial Expression Recognition
-
en_US
Pose Estimation
-
en_US
Viseme Recognition
-
en_US
SVM