Shape analysis of local facial patches for 3D facial expression recognition

Authors:
Ahmed Maalej;Boulbaba Ben Amor;Mohamed Daoudi;Anuj Srivastava;Stefano Berretti
Affiliations:
LIFL (UMR CNRS 8022), University of Lille 1, France and Institut TELECOM/ TELECOM Lille 1, France;LIFL (UMR CNRS 8022), University of Lille 1, France and Institut TELECOM/ TELECOM Lille 1, France;LIFL (UMR CNRS 8022), University of Lille 1, France and Institut TELECOM/ TELECOM Lille 1, France;Department of Statistics, Florida State University, USA;Dipartimento di Sistemi e Informatica, University of Firenze, Italy
Venue:
Pattern Recognition
Year:
2011

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Abstract

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.