| Título: | Towards Man-Machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis |
| Autores: | Kumar, Vinay P. |
| Fecha: |
2004-10-01 2004-10-01 2002-09-01 |
| Publicador: | MIT |
| Fuente: |
 |
| Tipo: | |
| Tema: |
AI Facial Expression Recognition Pose Estimation Viseme Recognition SVM |
| Descripción: | 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. |
| Idioma: | Inglés |
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