Deep Covariance Descriptors for Facial Expression Recognition

Naima Otberdout (1) , Anis Kacem (2, 3) , Mohamed Daoudi (2, 4) , Lahoucine Ballihi (1) , Stefano Berretti (5)

1 GSCM-LRIT - Laboratoire de Recherche en Informatique et Télécommunications [Rabat]
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 Université de Lille
4 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai
5 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence

In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial expression recognition. The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By performing the classification of the facial expressions using Gaussian kernel on SPD manifold, we show that the covariance descriptors computed on DCNN features are more efficient than the standard classification with fully connected layers and softmax. By implementing our approach using the VGG-face and ExpNet architectures with extensive experiments on the Oulu-CASIA and SFEW datasets, we show that the proposed approach achieves performance at the state of the art for facial expression recognition.

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

Naima Otberdout, Anis Kacem, Mohamed Daoudi, Lahoucine Ballihi, Stefano Berretti. Deep Covariance Descriptors for Facial Expression Recognition. British Machine Vision Conference, Sep 2018, NewCastle, United Kingdom. ⟨hal-01883248⟩