prof.irfanessa.com » 1994 http://prof.irfanessa.com Irfan Essa's Academic Activities Wed, 25 Jan 2012 23:42:09 +0000 en hourly 1 Thesis: Irfan Essa’s PhD Thesis (1994): “Analysis, interpretation and synthesis of facial expressions” http://prof.irfanessa.com/1994/08/30/dspace-at-mit-analysis-interpretation-and-synthesis-of-facial-expressions/?utm_source=rss&utm_medium=rss&utm_campaign=dspace-at-mit-analysis-interpretation-and-synthesis-of-facial-expressions http://prof.irfanessa.com/1994/08/30/dspace-at-mit-analysis-interpretation-and-synthesis-of-facial-expressions/#comments Tue, 30 Aug 1994 20:32:50 +0000 Irfan Essa http://essa.org/irfan/wp/?p=6 Irfan Essa (1994), “Analysis, interpretation and synthesis of facial expressions“, PhD Thesis, MIT, Cambridge, MA, USA. (Advisor: Alex (Sandy) Pentland

Irfan Essa’s PhD Thesis

]]> http://prof.irfanessa.com/1994/08/30/dspace-at-mit-analysis-interpretation-and-synthesis-of-facial-expressions/feed/ 3 Paper: IEEE CVPR (1994) “A vision system for observing and extracting facial action parameters” http://prof.irfanessa.com/1994/06/21/paper-ieee-cvpr-1994-a-vision-system-for-observing-and-extracting-facial-action-parameters/?utm_source=rss&utm_medium=rss&utm_campaign=paper-ieee-cvpr-1994-a-vision-system-for-observing-and-extracting-facial-action-parameters http://prof.irfanessa.com/1994/06/21/paper-ieee-cvpr-1994-a-vision-system-for-observing-and-extracting-facial-action-parameters/#comments Tue, 21 Jun 1994 18:57:21 +0000 Irfan Essa http://academics.irfanessa.com/?p=255 Essa, I.A. Pentland, A. (1994), “A vision system for observing and extracting facial action parameters”,
In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR ’94.),  21-23 June 1994, page(s): 76 – 83, 06/21/1994 – 06/23/1994, Seattle, WA, ISBN: 0-8186-5825-8 [Digital Object Identifier: 10.1109/CVPR.1994.323813][IEEEXplore#]

Abstract

We describe a computer vision system for observing the “action units” of a face using video sequences as input. The visual observation (sensing) is achieved by using an optimal estimation optical flow method coupled with a geometric and a physical (muscle) model describing the facial structure. This modeling results in a time-varying spatial patterning of facial shape and a parametric representation of the independent muscle action groups, responsible for the observed facial motions. These muscle action patterns may then be used for analysis, interpretation, and synthesis. Thus, by interpreting facial motions within a physics-based optimal estimation framework, a new control model of facial movement is developed. The newly extracted action units (which we name “FACS ”) are both physics and geometry-based, and extend the well-known FACS parameters for facial expressions by adding temporal information and non-local spatial patterning of facial motion

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