Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3638
Title: Deep Learning Based Facial Emotion Recognition System
Other Titles: Derin Ögrenme Tabanli Yüz Duygulari Tanima Sistemi
Authors: Ozdemir M.A.
Elagoz B.
Alaybeyoglu Soy A.
Akan A.
Keywords: Deep Learning
Emotion Recognition
Facial Expression
Biomedical engineering
Convolutional neural networks
Face recognition
Learning systems
Network architecture
Emotional state
Ethics committee
Facial emotions
Facial Expressions
Facial images
Image preprocessing
Learning methods
Training accuracy
Deep learning
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In this study, it was aimed to recognize the emotional state from facial images using the deep learning method. In the study, which was approved by the ethics committee, a custom data set was created using videos taken from 20 male and 20 female participants while simulating 7 different facial expressions (happy, sad, surprised, angry, disgusted, scared, and neutral). Firstly, obtained videos were divided into image frames, and then face images were segmented using the Haar library from image frames. The size of the custom data set obtained after the image preprocessing is more than 25 thousand images. The proposed convolutional neural network (CNN) architecture which is mimics of LeNet architecture has been trained with this custom dataset. According to the proposed CNN architecture experiment results, the training loss was found as 0.0115, the training accuracy was found as 99.62%, the validation loss was 0.0109, and the validation accuracy was 99.71%. © 2020 IEEE.
Description: 2020 Medical Technologies Congress, TIPTEKNO 2020 -- 19 November 2020 through 20 November 2020 -- 166140
URI: https://doi.org/10.1109/TIPTEKNO50054.2020.9299256
https://hdl.handle.net/20.500.14365/3638
ISBN: 9.78173E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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