Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3511
Title: Real-Time Facial Emotion Recognition for Visualization Systems
Authors: Ozkara C.
Ekim P.O.
Keywords: CNN
face detection
facial expression recognition
hybrid model
LSTM
Cameras
Convolutional neural networks
Emotion Recognition
Face recognition
Real time systems
Speech recognition
Convolutional neural network
Emotion recognition
Faces detection
Facial emotions
Facial expression recognition
Hybrid model
Neural networks algorithms
Performance
Real- time
Visualization system
Long short-term memory
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: This project aims to review the most popular deep learning algorithms and their performances in camera systems based on real-time facial emotion recognition and suggest a new model for future applications. Firstly, convolutional neural network (CNN) algorithms that recognize human emotions, such as AlexNet, GoogleNet, and VGG19, are investigated according to their performances. Then, the CNN algorithm with the best numerical performance is chosen for enhancement. After, the new hybrid model is constructed via chosen CNN and long short-term memory (LSTM). Lastly, the proposed model and face images achieved from the camera are combined to simulate real-time application. © 2022 IEEE.
Description: 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 -- 7 September 2022 through 9 September 2022 -- 183936
URI: https://doi.org/10.1109/ASYU56188.2022.9925465
https://hdl.handle.net/20.500.14365/3511
ISBN: 9.78167E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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