3D Dynamic Expression Recognition Based on a Novel Deformation Vector Field and Random Forest

Drira Hassen (1, 2) , Boulbaba Ben Amor (1, 2) , Daoudi Mohamed (1, 2) , Srivastava Anuj (3) , Stefano Berretti (4)

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

This paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial shape analysis and captures densely dynamic information from the entire face. The resulting temporal vector field is used to build the feature vector for expression recognition from 3D dynamic faces. By applying LDA-based feature space transformation for dimensionality reduction which is followed by a Multiclass Random Forest learning algorithm, the proposed approach achieved 93% average recognition rate on BU-4DFE database and outperforms state-of-art approaches.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Drira Hassen, Boulbaba Ben Amor, Daoudi Mohamed, Srivastava Anuj, Stefano Berretti. 3D Dynamic Expression Recognition Based on a Novel Deformation Vector Field and Random Forest. 21st International Conference on Pattern Recognition, Nov 2012, Tsukuba, Japan. https://iapr.papercept.net/conferences/scripts/abstract.pl?ConfID=7&Number=901. ⟨hal-00726185⟩