In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that computes SIFT descriptors on a set of facial landmarks of depth images, and then selects the subset of most relevant features. Using SVM classification of the selected features, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparative evaluation on a common experimental setup, shows that our solution is able to obtain state of the art results.
A Set of Selected SIFT Features for 3D Facial Expression Recognition / S. Berretti; A. Del Bimbo; P. Pala; B. Ben Amor; M. Daoudi. - STAMPA. - .:(2010), pp. 4125-4128. (Intervento presentato al convegno 20th International Conference on Pattern Recognition tenutosi a Istanbul, Turkey nel August 23-26) [10.1109/ICPR.2010.1002].
A Set of Selected SIFT Features for 3D Facial Expression Recognition
BERRETTI, STEFANO;DEL BIMBO, ALBERTO;PALA, PIETRO;
2010
Abstract
In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that computes SIFT descriptors on a set of facial landmarks of depth images, and then selects the subset of most relevant features. Using SVM classification of the selected features, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparative evaluation on a common experimental setup, shows that our solution is able to obtain state of the art results.
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