Facial Emotion Recognition Using Deep Learning Models Based on Transfer Learning Techniques with Classifier

Fouad Lehlou (), Adil El Makrani and Abdelaali Kemmou
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Fouad Lehlou: Ibn Tofail University
Adil El Makrani: Ibn Tofail University
Abdelaali Kemmou: Ibn Tofail University

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 224-231 from Springer

Abstract: Abstract Facial Expression Recognition (FER), also known as Facial Emotion Recognition, constitutes an actively discussed subject within the realms of computer vision and machine learning!-- Query ID="Q1" Text="This is to inform you that corresponding author has been identified as per the information available in the Copyright form.." -->.. It extends its influence into numerous disciplines, including education, psychology, human-computer interaction, and marketing research. The efficient recognition of facial expressions holds significant importance in addressing various challenges. This study undertakes a comprehensive exploration of facial emotion detection, employing the FER 2013 dataset. The study involves experimentation with four distinct convolutional neural network architectures: ResNet-V2, MobileNet-V3, Sequential, and Inception-V3. The primary objective is to categorize seven distinct emotions, namely anger, fear, disgust, happiness, surprise, sadness, and neutrality. The outcomes of the experiments conducted on the FER-2013 Dataset reveal that the fine-tuned MobileNet-V3 model outperforms the other methods in terms of performance.

Keywords: Deep Learning; FER-2013; Inception-V3; Machine Learning; MobilNet-V3; ResNet-V2 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_25

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DOI: 10.1007/978-3-031-75329-9_25

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