Title :

Facial Expression Recognition Based on DWT Feature for Deep CNN

Author(s) :

Bendjillali, Ridha [Auteur]
Beladgham, Mohammed [Auteur]
Merit, Khaled [Auteur]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Alouani, Lihsen [Auteur]
INSA Institut National des Sciences Appliquées Hauts-de-France [INSA Hauts-De-France]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]

Conference title :

6th International Conference on Control, Decision and Information Technologies (CoDIT 2019)

City :

Paris

Country :

France

Start date of the conference :

2019-04-23

Publisher :

IEEE

English keyword(s) :

Discrete wavelet transforms
Convolution
Face recognition
Face
Databases
Feature extraction

HAL domain(s) :

Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique

English abstract : [en]

Facial expressions recognition have become one of the most important fields of research in pattern recognition, in this paper, we propose a method to identify the facial expressions of the people through their emotions, ...
Show more >Facial expressions recognition have become one of the most important fields of research in pattern recognition, in this paper, we propose a method to identify the facial expressions of the people through their emotions, this method combining Viola-Jones face detection algorithm, Facial image enhancement using histogram equalization, discrete wavelet transform (DWT) and deep convolution neural network. Extraction results of facial features using DWT are the input of CNN, which are used directly to train the CNN network. Our experimental were performed on CK+ database and JAFFE face database, the obtained results based on this network is 96.46% and 98.43% respectively.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL