| Title | Facial Emotion Recognition using Deep Learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | R, Sowmiya, G, Sivakamasundari, V, Archana |
| Conference Name | 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) |
| Date Published | dec |
| Keywords | Automation, convolutional neural network (CNN), Deep Learning, DenseNet-169, emotion recognition, face recognition, facial recognition, feature extraction, Human Behavior, Human Facial Emotion Recognition, Metrics, Neural networks, pubcrawl, renewable energy sources, resilience, Resiliency |
| Abstract | Human facial emotion recognition pays a variety of applications in society. The basic idea of Facial Emotion Recognition is to map the different facial emotions to a variety of emotional states. Conventional Facial Emotion Recognition consists of two processes: extracting the features and feature selection. Nowadays, in deep learning algorithms, Convolutional Neural Networks are primarily used in Facial Emotion Recognition because of their hidden feature extraction from the images. Usually, the standard Convolutional Neural Network has simple learning algorithms with finite feature extraction layers for extracting information. The drawback of the earlier approach was that they validated only the frontal view of the photos even though the image was obtained from different angles. This research work uses a deep Convolutional Neural Network along with a DenseNet-169 as a backbone network for recognizing facial emotions. The emotion Recognition dataset was used to recognize the emotions with an accuracy of 96%. |
| DOI | 10.1109/ICACRS55517.2022.10029092 |
| Citation Key | r_facial_2022 |
Facial Emotion Recognition using Deep Learning Approach