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Facial Expression Recognition Based on DWT Feature for Deep CNN

Ridha Bendjillali Mohammed Beladgham Khaled Merit Abdelmalik Taleb-Ahmed 1, 2 Ihsen Alouani 1, 2 
1 COMNUM - IEMN - COMmunications NUMériques - IEMN
IEMN-DOAE - Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520
Abstract : 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.
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https://hal-uphf.archives-ouvertes.fr/hal-03572167
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Submitted on : Monday, February 14, 2022 - 10:36:16 AM
Last modification on : Wednesday, March 23, 2022 - 3:51:16 PM

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Ridha Bendjillali, Mohammed Beladgham, Khaled Merit, Abdelmalik Taleb-Ahmed, Ihsen Alouani. Facial Expression Recognition Based on DWT Feature for Deep CNN. 6th International Conference on Control, Decision and Information Technologies (CoDIT 2019), Apr 2019, Paris, France. pp.344-348, ⟨10.1109/CoDIT.2019.8820410⟩. ⟨hal-03572167⟩

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