CNN; Convolutional Neural Network; Data Preprocessing; Deep Learning; Facial Emotion Recognition; Facial Expression Recognition; FER; Image Recognition; Face recognition; Speech recognition; Emotion recognition; Facial emotions; Facial Expressions; Human faces; Learning techniques; Convolutional neural networks
[en] Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers. With the growing number of challenging datasets, the application of deep learning techniques have all become necessary. In this paper, we study the challenges of Emotion Recognition Datasets and we also try different parameters and architectures of the Conventional Neural Networks (CNNs) in order to detect the seven emotions in human faces, such as: anger, fear, disgust, contempt, happiness, sadness and surprise. We have chosen iCV MEFED (Multi-Emotion Facial Expression Dataset) as the main dataset for our study, which is relatively new, interesting and very challenging. © 2020 IEEE.
Event name :
1st International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2020
Journal title :
Proceedings - 2020 International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2020