Statistical synthesis of facial expressions for the portrayal of emotion

Gralewski, Lisa; Campbell, Neill; Thomas, Barry; Dalton, Colin J; Gibson, David

doi:10.1145/988834.988867

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…For details on ROC analysis see Section 4.1.2. Within the field of facial expression synthesis, a number of approaches have been reported in the literature over the past 10 years (Raouzaiou et al, 2002;Zhang et al, 2006;Wang & Ahuja, 2003;Choe & Ko, 2001;Gralewski, 2004;Ghent, 2005a;Ghent 2005b). The approach presented in this chapter combines the FACS, statistical shape and texture models, and machine learning techniques to provide a novel solution to this problem.…”

Section: Computational Approaches For Facial Expression Analysismentioning

confidence: 99%

Modelling, Classification and Synthesis of Facial Expressions

Reilly¹,

Ghent²,

McDonald³

2008

Affective Computing

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Section: Computational Approaches For Facial Expression Analysismentioning

confidence: 99%

Modelling, Classification and Synthesis of Facial Expressions

Reilly¹,

Ghent²,

McDonald³

2008

Affective Computing

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“…The real-time system developed uses a model based nonrigid face tracking algorithm to extract motion features that serve as input to a classifier used for recognizing the different facial expressions. [11] work has been focused towards developing a tool for animators, which will enable quick and easy generation of draft scenes whereby an animated character or characters may undergo several transitions between emotions. The approach adopted in this work is to provide an `expression space', which an animator can navigate, and as they move throughout this space, a new sequence corresponding to the selected emotion is synthesized and generated automatically.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression as an Implicit Customers' Feedback and the Challenges

Kasiran

Yahya

2007

Computer Graphics, Imaging and Visualisation (CGIV 2007)

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The human face is rich of information and plays important roles in daily communication such as expressing the emotions nonverbally. Facial expression comes in all varieties. Some are intense and sustained while others are subtle and fleeting. Much progress has been made to build computer systems that recognize facial expression for human computer interaction such as affective computing which apply the automatic facial recognition techniques in human computer interaction where the main idea is that the computer could better adjust its behavior to user's current emotion. Other possible area that could use the advance technology of Facial expression recognition system is the customer satisfaction measurement. The expression of customer being served at the counter is captured to evaluate the satisfaction of the customer. This multimedia approach of customer satisfaction measurement is an alternative of the conventional way of collecting customers' response.

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“…There has been a lot of work in this area over the past 5 years [1][2][3][4][5]. However, real-time photo-realistic expression synthesis for unseen images has not yet been achieved [6].…”

Section: Introductionmentioning

confidence: 99%

Photo-realistic facial expression synthesis

Ghent

McDonald

2005

Image and Vision Computing

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This paper details a procedure for generating a function which maps an image of a neutral face to one depicting a desired expression independent of age, sex, or skin colour. Facial expression synthesis is a growing and relatively new domain within computer vision. One of the fundamental problems when trying to produce accurate expression synthesis in previous approaches is the lack of a consistent method for measuring expression. This inhibits the generation of a universal mapping function. This paper advances this domain by the introduction of the Facial Expression Shape Model (FESM) and the Facial Expression Texture Model (FETM). These are statistical models of facial expression based on anatomical analysis of expression called the Facial Action Coding System (FACS). The FESM and the FETM allow for the generation of a universal mapping function. These models provide a robust means for upholding the rules of the FACS and are flexible enough to describe subjects that are not present during the training phase. We use these models in conjunction with several Artificial Neural Networks (ANN) to generate photo-realistic images of facial expressions. q

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Statistical synthesis of facial expressions for the portrayal of emotion

Cited by 11 publications

References 25 publications

Modelling, Classification and Synthesis of Facial Expressions

Modelling, Classification and Synthesis of Facial Expressions

Facial Expression as an Implicit Customers' Feedback and the Challenges

Photo-realistic facial expression synthesis

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