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
uri: http://hdl.handle.net/1721.1/5569
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

Item sets: AI Technical Reports (1964 - 2004)