| dc.creator |
Kumar, Vinay P. |
|
| dc.date |
2004-10-01T14:00:07Z |
|
| dc.date |
2004-10-01T14:00:07Z |
|
| dc.date |
2002-09-01 |
|
| dc.date.accessioned |
2013-10-09T02:40:09Z |
|
| dc.date.available |
2013-10-09T02:40:09Z |
|
| dc.date.issued |
2013-10-09 |
|
| dc.identifier |
AITR-2002-008 |
|
| dc.identifier |
CBCL-221 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/5569 |
|
| dc.identifier.uri |
http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
|
| dc.description |
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. |
|
| dc.format |
68 p. |
|
| dc.format |
21293042 bytes |
|
| dc.format |
2473001 bytes |
|
| dc.format |
application/postscript |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.relation |
AITR-2002-008 |
|
| dc.relation |
CBCL-221 |
|
| dc.subject |
AI |
|
| dc.subject |
Facial Expression Recognition |
|
| dc.subject |
Pose Estimation |
|
| dc.subject |
Viseme Recognition |
|
| dc.subject |
SVM |
|
| dc.title |
Towards Man-Machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis |
|