Journal
PATTERN RECOGNITION
Volume 44, Issue 8, Pages 1581-1589Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.02.012
Keywords
3D facial expression classification; Shape analysis; Geodesic path; Multiboosting; SVM
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In this paper we address the problem of 3D facial expression recognition. We propose a local geometric shape analysis of facial surfaces coupled with machine learning techniques for expression classification. A computation of the length of the geodesic path between corresponding patches, using a Riemannian framework, in a shape space provides a quantitative information about their similarities. These measures are then used as inputs to several classification methods. The experimental results demonstrate the effectiveness of the proposed approach. Using multiboosting and support vector machines (SVM) classifiers, we achieved 98.81% and 97.75% recognition average rates, respectively, for recognition of the six prototypical facial expressions on BU-3DFE database. A comparative study using the same experimental setting shows that the suggested approach outperforms previous work. (C) 2011 Elsevier Ltd. All rights reserved.
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