Visible to the public Emotion Recognition Based on Facial Expressions Using Convolutional Neural Network (CNN)

TitleEmotion Recognition Based on Facial Expressions Using Convolutional Neural Network (CNN)
Publication TypeConference Paper
Year of Publication2020
AuthorsBegaj, S., Topal, A. O., Ali, M.
Conference Name2020 International Conference on Computing, Networking, Telecommunications Engineering Sciences Applications (CoNTESA)
Date PublishedDecember 2020
ISBN Number978-1-7281-8488-3
KeywordsCNN, convolutional neural nets, convolutional neural network, Data preprocessing, Deep Learning, deep learning (artificial intelligence), emotion detection, emotion recognition, emotion recognition datasets, face recognition, faces, facial emotion recognition, facial expression recognition, facial recognition, feature extraction, FER, Human Behavior, human faces, iCV MEFED, image recognition, lighting, Metrics, multiemotion facial expression dataset, psychology, pubcrawl, resilience, Resiliency, Training data

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.

Citation Keybegaj_emotion_2020