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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Simultaneous Identity and Expression Recognition Using Face Geometry on Low Dimensional Manifolds
verfasst von : Pedro Martins, Jorge Batista
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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A solution for simultaneous identity and expression recognition is proposed. The proposed solution starts by extracting face geometry from input images using Active Appearance Models (AAM). Low dimensional manifolds were then derived using Laplacian EigenMaps resulting in two types of manifolds, one for model identity and the other for expression. Respective multiclass Support Vector Machines (SVM) were trained. The recognition is composed by a two step cascade, where first the identity is predicted and then its associated expression model is used to predict the facial expression. For evaluation proposes a database was build consisting on 6770 images captured from 4 people exhibiting 7 different emotions. The identity overall recognition rate was 96.8%. Facial expression results are identity dependent, and the most expressive individual achieves 76.8% of overall recognition rate.