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1 LIFL - FOX MIIRE 2 Institut TELECOM-TELECOM Lille1 3 Department of Statistics Tallahassee, FL 4 DSI - Dipartimento di Sistemi e Informatica

Abstract : We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces are extracted and represented by sets of closed curves. A Riemannian framework is used to derive the shape analysis of the extracted patches. The applied framework permits to calculate a similarity or dissimilarity distances between patches, and to compute the optimal deformation between them. Once calculated, these measures are employed as inputs to a commonly used classification techniques such as AdaBoost and Support Vector Machines SVM. A quantitative evaluation of our novel approach is conducted on a subset of the publicly available BU-3DFE database.

Keywords : 3D facial expression recognition binary classification shape analysis





Autor: Ahmed Maalej - Boulbaba Ben Amor - Mohamed Daoudi - Anuj Srivastava - Stefano Berretti -

Fuente: https://hal.archives-ouvertes.fr/



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