In this paper, we present a fully automatic approach for identity-independent facial expression recognition from 3D video sequences. Towards this goal, we propose a novel approach to extract a dense scalar field that represents the deformations between faces conveying different expressions. We extract relevant features from this deformation field using LDA and then train a dynamic model on these features using HMM. Experiments conducted on BU-4DFE dataset following state-of-the-art settings show the effectiveness of the proposed approach.
A Dense Deformation Field for Facial Expression Analysis in Dynamic Sequences of 3D Scans / Hassen Drira; Boulbaba Ben Amor; Mohamed Daoudi; Stefano Berretti. - STAMPA. - LNCS 8212:(2013), pp. 148-159. (Intervento presentato al convegno 4th International Workshop on Human Behavior Understanding tenutosi a Barcellona nel 22 ottobre 2013) [10.1007/978-3-319-02714-2_13].
A Dense Deformation Field for Facial Expression Analysis in Dynamic Sequences of 3D Scans
BERRETTI, STEFANO
2013
Abstract
In this paper, we present a fully automatic approach for identity-independent facial expression recognition from 3D video sequences. Towards this goal, we propose a novel approach to extract a dense scalar field that represents the deformations between faces conveying different expressions. We extract relevant features from this deformation field using LDA and then train a dynamic model on these features using HMM. Experiments conducted on BU-4DFE dataset following state-of-the-art settings show the effectiveness of the proposed approach.File | Dimensione | Formato | |
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