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Facial Feature Tracking and Expression Recognition for Sign Language: Automatic recognition of common facial gestures in sign languages using single camera input - Couverture souple

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9783838327136: Facial Feature Tracking and Expression Recognition for Sign Language: Automatic recognition of common facial gestures in sign languages using single camera input
Couverture souple
ISBN 10 : 3838327136 ISBN 13 : 9783838327136
Editeur : LAP Lambert Academic Publishing, 2009

Synopsis

The focus of this work is on classifying the most common non-manual (facial) gestures in Sign Language. This goal is achieved in two consecutive steps: First, automatic facial landmarking is performed based on Multi-resolution Active Shape Models (MRASMs). Second, the tracked landmarks are normalized and expression classification is done based on multivariate Continuous Hidden Markov Model (CHMMs). We collected a video database of expressions from Turkish Sign Language (TSL) to test the proposed approach. The expressions used are universal and the results are applicable to other sign languages. Single view vs. multi-view and person specific vs. generic MRASM trackers are compared both for tracking and expression recognition. The multi-view person-specific tracker performs the best and tracks the landmarks robustly. For expression classification, the proposed CHMM classifier is tested on different training and test set combinations and the results are reported. We observe that the classification performances of distinct classes are very high.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

The focus of this work is on classifying the most common non-manual (facial) gestures in Sign Language. This goal is achieved in two consecutive steps: First, automatic facial landmarking is performed based on Multi-resolution Active Shape Models (MRASMs). Second, the tracked landmarks are normalized and expression classification is done based on multivariate Continuous Hidden Markov Model (CHMMs). We collected a video database of expressions from Turkish Sign Language (TSL) to test the proposed approach. The expressions used are universal and the results are applicable to other sign languages. Single view vs. multi-view and person specific vs. generic MRASM trackers are compared both for tracking and expression recognition. The multi-view person-specific tracker performs the best and tracks the landmarks robustly. For expression classification, the proposed CHMM classifier is tested on different training and test set combinations and the results are reported. We observe that the classification performances of distinct classes are very high.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP Lambert Academic Publishing
Date d'édition
2009
Langue
anglais
ISBN 10 
3838327136
ISBN 13 
9783838327136
Reliure
Broché
Nombre de pages
96
Coordonnées du fabricant
non disponible
Personne responsable
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