Person independent 3D facial expression recognition by a selected ensemble of SIFT descriptors

Authors:
Stefano Berretti;Boulbaba Ben Amor;Mohamed Daoudi;Alberto Del Bimbo
Affiliations:
Dipartimento di Sistemi e Informatica, University of Firenze, Firenze, Italy;Institut TELECOM, TELECOM Lille 1, LIFL, UMR, France;Institut TELECOM, TELECOM Lille 1, LIFL, UMR, France;Dipartimento di Sistemi e Informatica, University of Firenze, Firenze, Italy
Venue:
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Year:
2010

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Abstract

Facial expression recognition has been addressed mainly working on 2D images or videos. 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 relies on selecting the minimal-redundancy maximal-relevance features derived from a pool of SIFT feature descriptors computed in correspondence with facial landmarks of depth images. Training a Support Vector Machine for every basic facial expression to be recognized, and combining them to form a multiclass classifier, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparison with competitors approaches using a common experimental setting on the BU-3DFE database, shows that our solution is able to obtain state of the art results.