Investigating the Dynamics of Facial Expression

Reilly, Jane; Ghent, John; McDonald, John

doi:10.1007/11919629_35

Cited by 21 publications

(18 citation statements)

References 27 publications

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“…Assuming these movements as being important in the context of non-verbal communication, it is necessary to ponder methods that include facial motion. Several researchers have claimed that not only the movement itself, but also the timing associated with facial motions are critical parameters in recognizing emotions (Cockburn et al, 2008;Ekman, 1993;Fasel & Luettin, 2003;J. Reilly, 2006).…”

Section: Challenges In Trainning Methodologiesmentioning

confidence: 99%

Interactive Technology: Teaching People with Autism to Recognize Facial Emotions

Fernandes¹,

Sousa²,

Orvalho³

2011

Autism Spectrum Disorders - From Genes to Environment

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Section: Challenges In Trainning Methodologiesmentioning

confidence: 99%

Interactive Technology: Teaching People with Autism to Recognize Facial Emotions

Fernandes¹,

Sousa²,

Orvalho³

2011

Autism Spectrum Disorders - From Genes to Environment

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“…Following on from this, in Section 5 we demonstrate the success of our technique for modelling the dynamics of facial expression, discussing some experiments and work to date. The research presented in this paper builds on our previous works presented in [18,19] and [20].…”

Section: Introductionmentioning

confidence: 93%

Automatic estimation of the dynamics of facial expression using a three-level model of intensity

Delannoy

McDonald

2008

2008 8th IEEE International Conference on Automatic Face &Amp; Gesture Recognition

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Facial expressions and their associated dynamics play an important role in human communication. The dynamics of facial expressions can be defined as the intensity and timing of their constituent components as they form. However, estimating the dynamics of facial expressions is a non trivial task. The majority of automatic approaches to characterising intensity use a two-level model (also known as onset-apex-offset). However the FACS specifies five intensity levels for each AU. In this paper we evaluate the efficacy of Local Linear Embedding as a means of estimating the intensity of facial expression. This is done using both the full five level FACS model, and a simplified three level model. We have found that using the FACS intensity scoring results in a considerable overlap between the estimated intensities. Using a three level model enables us to classify the intensities with significantly greater degree of accuracy.

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“…Following on from this we demonstrate the success of our technique for modelling the dynamics of facial expression, discussing some experiments and work to date in Section 4. The research presented in this paper builds on our previous works presented in [18,19] and [20].…”

Section: Introductionmentioning

confidence: 93%

Modelling the Manifold of Facial Expression Using Texture

Reilly

McDonald

2008

2008 International Machine Vision and Image Processing Conference

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The speed and intensity of the appearance changes that occur during the formation of facial expressions provide important information about the underlying meaning of the expression itself. In the past we have demonstrated the effectiveness of using Locally Linear Embedding with facial shape information for estimating the dynamics of facial expression. This approach was only suitable for specific expressions, where the appearance change was principally due to a movement or distortion of the shape of facial features. However, for some facial expressions, the variation in the shape of the facial features is very subtle. These expressions are mainly characterised by the variation in the texture of the face. Hence such expressions are not amenable to the previous approach. In order to estimate the dynamics of these types of expressions it is necessary to develop nonlinear appearance models that incorporate texture information. In this paper we use LLE to estimate the manifold of texture variation due to facial expression. We show that the resulting manifold effectively captures the underlying dynamics of facial expression and that it provides a suitable representation for differentiation between posed and spontaneous expressions.

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Investigating the Dynamics of Facial Expression

Cited by 21 publications

References 27 publications

Interactive Technology: Teaching People with Autism to Recognize Facial Emotions

Interactive Technology: Teaching People with Autism to Recognize Facial Emotions

Automatic estimation of the dynamics of facial expression using a three-level model of intensity

Modelling the Manifold of Facial Expression Using Texture

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