Emotion Recognition Based on Facial Expressions Using Convolutional Neural Network (CNN)
Title | Emotion Recognition Based on Facial Expressions Using Convolutional Neural Network (CNN) |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Begaj, S., Topal, A. O., Ali, M. |
Conference Name | 2020 International Conference on Computing, Networking, Telecommunications Engineering Sciences Applications (CoNTESA) |
Date Published | December 2020 |
Publisher | IEEE |
ISBN Number | 978-1-7281-8488-3 |
Keywords | CNN, 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 |
Abstract | 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. |
URL | https://ieeexplore.ieee.org/document/9302866 |
DOI | 10.1109/CoNTESA50436.2020.9302866 |
Citation Key | begaj_emotion_2020 |
- feature extraction
- Training data
- Resiliency
- resilience
- pubcrawl
- psychology
- multiemotion facial expression dataset
- Metrics
- lighting
- image recognition
- iCV MEFED
- human faces
- Human behavior
- FER
- CNN
- facial recognition
- facial expression recognition
- facial emotion recognition
- faces
- face recognition
- emotion recognition datasets
- emotion recognition
- emotion detection
- deep learning (artificial intelligence)
- deep learning
- Data preprocessing
- convolutional neural network
- convolutional neural nets