Real-Time Facial Emotion Recognition for Visualization Systems
| dc.contributor.author | Ozkara C. | |
| dc.contributor.author | Ekim P.O. | |
| dc.date.accessioned | 2023-06-16T14:59:33Z | |
| dc.date.available | 2023-06-16T14:59:33Z | |
| dc.date.issued | 2022 | |
| dc.description | 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 -- 7 September 2022 through 9 September 2022 -- 183936 | en_US |
| dc.description.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. | en_US |
| dc.identifier.doi | 10.1109/ASYU56188.2022.9925465 | |
| dc.identifier.isbn | 9.78E+12 | |
| dc.identifier.scopus | 2-s2.0-85142765138 | |
| dc.identifier.uri | https://doi.org/10.1109/ASYU56188.2022.9925465 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14365/3511 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | Proceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | CNN | en_US |
| dc.subject | face detection | en_US |
| dc.subject | facial expression recognition | en_US |
| dc.subject | hybrid model | en_US |
| dc.subject | LSTM | en_US |
| dc.subject | Cameras | en_US |
| dc.subject | Convolutional neural networks | en_US |
| dc.subject | Emotion Recognition | en_US |
| dc.subject | Face recognition | en_US |
| dc.subject | Real time systems | en_US |
| dc.subject | Speech recognition | en_US |
| dc.subject | Convolutional neural network | en_US |
| dc.subject | Emotion recognition | en_US |
| dc.subject | Faces detection | en_US |
| dc.subject | Facial emotions | en_US |
| dc.subject | Facial expression recognition | en_US |
| dc.subject | Hybrid model | en_US |
| dc.subject | Neural networks algorithms | en_US |
| dc.subject | Performance | en_US |
| dc.subject | Real- time | en_US |
| dc.subject | Visualization system | en_US |
| dc.subject | Long short-term memory | en_US |
| dc.title | Real-Time Facial Emotion Recognition for Visualization Systems | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
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| gdc.description.departmenttemp | Ozkara, C., Izmir University of Economics, Izmir, Turkey; Ekim, P.O., Izmir University of Economics, Izmir, Turkey | en_US |
| gdc.description.endpage | 5 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 1 | |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W4313014723 | |
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| gdc.oaire.sciencefields | 0202 electrical engineering, electronic engineering, information engineering | |
| gdc.oaire.sciencefields | 02 engineering and technology | |
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