Facial Emotion Recognition through Neural Networks
Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.
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- Akhil Kumar, Arvind Kalia, . A. S. 2020. Object detection: A comprehensive review of the state-of-the-art methods. International Journal of Next-Generation Computing (IJNGC) 11, 1, pp.52–75.
- Aote, Shailendra, A. M. A. K. Y. D. G. S. and Kapse, J. 2021. Emotion-based media recommendation system. International Journal of Next-Generation Computing (IJNGC) 12, 5, pp.1–2. DOI: https://doi.org/10.47164/ijngc.v12i5.415
- Aparna Ambadas Joshi, V. C. and Kaveri, P. 2021. Effect of changing distances for extracting image information for error reduction of mouth features. International Journal of Next-Generation Computing (IJNGC) 12, 2, pp.270–279.
- Balasubramanian, B., Diwan, P., Nadar, R., and Bhatia, A. 2019. Analysis of facial emotion recognition. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, pp.945–949. DOI: https://doi.org/10.1109/ICOEI.2019.8862731
- Bhardwaj, R. J. . and Rao, D. 2022. Modified neural network-based object classification in video surveillance system. International Journal of Next-Generation Computing (IJNGC) 13, 3. DOI: https://doi.org/10.47164/ijngc.v13i3.890
- Goyani, M. M. 2019. Web enabled spontaneous facial expression database (wesfed): Challenges and design. International Journal of Next-Generation Computing (IJNGC) 10, 2, pp.139–151.
- Goyani, M. M. . and Patel, N. M. . 2017. Judgmental feature based facial expression recognition and fer datasets - a comprehensive study. International Journal of Next-Generation Computing (IJNGC) 8, 1, pp.62–81.
- Mehendale, N. 2020. Facial emotion recognition using convolutional neural networks (ferc).SN Applied Sciences 2, 3, pp.1–8. DOI: https://doi.org/10.1007/s42452-020-2234-1
- R. Kaur, D. A. . J. and Sharma, D. A. . 2022. The design and development of a flower classification hybrid model for feature extraction using cnn and intersection with machine learning with and without optimization techniques. International Journal of Next-Generation Computing (IJNGC) 13, 3, pp.1–2. DOI: https://doi.org/10.47164/ijngc.v13i3.663
- Russell, J. A. 1994. Is there universal recognition of emotion from facial expression? a review of the cross-cultural studies. Psychological bulletin 115, 1, pp.102. DOI: https://doi.org/10.1037/0033-2909.115.1.102
- Sinha, D. and El-Sharkawy, M. 2019. Thin mobilenet: An enhanced mobilenet architecture. In 2019 IEEE 10th annual ubiquitous computing, electronics & mobile communicationconference (UEMCON). IEEE, pp.0280–0285 DOI: https://doi.org/10.1109/UEMCON47517.2019.8993089