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Electronics 2019, 8(3), 324; https://doi.org/10.3390/electronics8030324

Improved Facial Expression Recognition Based on DWT Feature for Deep CNN

1
Laboratory of TIT, Department of Electrical Engineering, Tahri Mohammed University, Bechar 08000, Algeria
2
Laboratory of IEMN DOAE. UMR CNRS 852, University of Valenciennes, 59313 Valenciennes, France
*
Author to whom correspondence should be addressed.
Received: 18 January 2019 / Revised: 6 March 2019 / Accepted: 8 March 2019 / Published: 15 March 2019
(This article belongs to the Section Computer Science & Engineering)
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Abstract

Facial expression recognition (FER) has become one of the most important fields of research in pattern recognition. In this paper, we propose a method for the identification of facial expressions of people through their emotions. Being robust against illumination changes, this method combines four steps: Viola–Jones face detection algorithm, facial image enhancement using contrast limited adaptive histogram equalization (CLAHE) algorithm, the discrete wavelet transform (DWT), and deep convolutional neural network (CNN). We have used Viola–Jones to locate the face and facial parts; the facial image is enhanced using CLAHE; then facial features extraction is done using DWT; and finally, the extracted features are used directly to train the CNN network, for the purpose of classifying the facial expressions. Our experimental work was performed on the CK+ database and JAFFE face database. The results obtained using this network were 96.46% and 98.43%, respectively. View Full-Text
Keywords: facial expression recognition (FER); deep convolutional neural network (deep CNN); discrete wavelet transform (DWT); contrast limited adaptive histogram equalization (CLAHE) facial expression recognition (FER); deep convolutional neural network (deep CNN); discrete wavelet transform (DWT); contrast limited adaptive histogram equalization (CLAHE)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Bendjillali, R.I.; Beladgham, M.; Merit, K.; Taleb-Ahmed, A. Improved Facial Expression Recognition Based on DWT Feature for Deep CNN. Electronics 2019, 8, 324.

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