| Title | Facial Expression Recognition Using CNN |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Sadikoğlu, Fahreddin M., Idle Mohamed, Mohamed |
| Conference Name | 2022 International Conference on Artificial Intelligence in Everything (AIE) |
| Keywords | convolutional neural networks, Databases, Deep Learning, face recognition, facial expression analysis, facial expression recognition, facial expressions, facial recognition, Human Behavior, icv-MEFED, image recognition, Metrics, pubcrawl, resilience, Resiliency, System performance, Training, transfer learning |
| Abstract | Facial is the most dynamic part of the human body that conveys information about emotions. The level of diversity in facial geometry and facial look makes it possible to detect various human expressions. To be able to differentiate among numerous facial expressions of emotion, it is crucial to identify the classes of facial expressions. The methodology used in this article is based on convolutional neural networks (CNN). In this paper Deep Learning CNN is used to examine Alex net architectures. Improvements were achieved by applying the transfer learning approach and modifying the fully connected layer with the Support Vector Machine(SVM) classifier. The system succeeded by achieving satisfactory results on icv-the MEFED dataset. Improved models achieved around 64.29 %of recognition rates for the classification of the selected expressions. The results obtained are acceptable and comparable to the relevant systems in the literature provide ideas a background for further improvements. |
| DOI | 10.1109/AIE57029.2022.00025 |
| Citation Key | sadikoglu_facial_2022 |
Facial Expression Recognition Using CNN