Literature DB >> 15484916

Facial action recognition for facial expression analysis from static face images.

Maja Pantic1, Leon J M Rothkrantz.   

Abstract

Automatic recognition of facial gestures (i.e., facial muscle activity) is rapidly becoming an area of intense interest in the research field of machine vision. In this paper, we present an automated system that we developed to recognize facial gestures in static, frontal- and/or profile-view color face images. A multidetector approach to facial feature localization is utilized to spatially sample the profile contour and the contours of the facial components such as the eyes and the mouth. From the extracted contours of the facial features, we extract ten profile-contour fiducial points and 19 fiducial points of the contours of the facial components. Based on these, 32 individual facial muscle actions (AUs) occurring alone or in combination are recognized using rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.

Mesh:

Year:  2004        PMID: 15484916     DOI: 10.1109/tsmcb.2004.825931

Source DB:  PubMed          Journal:  IEEE Trans Syst Man Cybern B Cybern        ISSN: 1083-4419


  10 in total

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4.  Dynamic Cascades with Bidirectional Bootstrapping for Action Unit Detection in Spontaneous Facial Behavior.

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5.  The Painful Face - Pain Expression Recognition Using Active Appearance Models.

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6.  Alterations in facial expressivity in youth at clinical high-risk for psychosis.

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7.  Automated Facial Expression Recognition Framework Using Deep Learning.

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8.  Signing with the Face: Emotional Expression in Narrative Production in Deaf Children with Autism Spectrum Disorder.

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9.  Facial expression recognition and histograms of oriented gradients: a comprehensive study.

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Journal:  Springerplus       Date:  2015-10-26

10.  Automatic Infants' Pain Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational Age, Gender, and Race.

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  10 in total

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