Title :

Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories

Author(s) :

Otberdout, Naima [Auteur]
Laboratoire de Recherche en Informatique et Télécommunications [Rabat] [GSCM-LRIT]
Kacem, Anis [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Daoudi, Mohamed [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ballihi, Lahoucine [Auteur]
Laboratoire de Recherche en Informatique et Télécommunications [Rabat] [GSCM-LRIT]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]

Journal title :

IEEE Transactions on Neural Networks and Learning Systems

Pages :

3892-3905

Publisher :

IEEE

Publication date :

2020-10-01

ISSN :

2162-237X

English keyword(s) :

Index Terms-Convolutional neural networks
deep learning
symmetric positive definite manifold
Convolutional neural networks
covariance matrix
facial expression recognition

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Convolutional Neural Network (DCNN) features ...
Show more >In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Convolutional Neural Network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By conducting the classification of static facial expressions using Support Vector Machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, SFEW and AFEW datasets, we show that both the proposed static and dynamic approaches achieve state-of-the-art performance for facial expression recognition outperforming many recent approaches.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

ULNE

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL