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Article Dans Une Revue Electronics Année : 2019

Improved facial expression recognition based on DWT feature for deep CNN

Résumé

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\textendash 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\textendash 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.
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Dates et versions

hal-03143597 , version 1 (16-02-2021)

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Ridha Bendjillali, Mohammed Beladgham, Khaled Merit, Abdelmalik Taleb-Ahmed. Improved facial expression recognition based on DWT feature for deep CNN. Electronics, 2019, 8 (3), pp.324. ⟨10.3390/electronics8030324⟩. ⟨hal-03143597⟩
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