Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 02015 | |
| Number of page(s) | 6 | |
| Section | Machine Learning Applications in Vision, Security, and Healthcare | |
| DOI | https://doi.org/10.1051/itmconf/20257802015 | |
| Published online | 08 September 2025 | |
- A. Khattak, M. Z. Asghar, M. Ali et al., "An efficient deep learning technique for facial emotion recognition," Multimedia Tools Appl., vol. 81, no. 2, pp. 1649–1683, 2022. [Google Scholar]
- S. Minaee, M. Minaei and A. Abdolrashidi, "Deep-emotion: Facial expression recognition using attentional convolutional network," Sensors, vol. 21, no. 9, Art. no. 3046, 2021. [Google Scholar]
- J. S. Angel, A. DianaAndrushia, T. M. Neebha et al., "Faster Region Convolutional Neural Network (FRCNN) based facial emotion recognition," Comput. Mater. Continua, vol. 79, no. 2, 2024. [Google Scholar]
- Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998. [CrossRef] [Google Scholar]
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint, arXiv:1409.1556, 2014. [Google Scholar]
- K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016. [Google Scholar]
- I. J. Goodfellow et al., "Challenges in representation learning: A report on three machine learning contests," in Proc. Int. Conf. Neural Inf. Process. (ICONIP), Daegu, Korea, 2013, pp. 117–124. [Google Scholar]
- P. Ekman and W. V. Friesen, "Constants across cultures in the face and emotion," J. Pers. Soc. Psychol., vol. 17, no. 2, pp. 124–129, 1971. [Google Scholar]
- C. Szegedy, S. Ioffe, V. Vanhoucke and A. Alemi, "Inception-v4, inception-resnet and the impact of residual connections on learning," in Proc. AAAI Conf. Artif. Intell., vol. 31, no. 1, 2017. [Google Scholar]
- D. Tran, L. Bourdev, R. Fergus, L. Torresani and M. Paluri, "Learning spatiotemporal features with 3D convolutional networks," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2015, pp. 4489–4497. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.