Facial Expression Recognition Using Pre-trained Architectures
Author:
Affiliation:
1. Department of Computer Science, CHRIST University, Bangalore 560029, India
2. Software Engineer, Idea Elan India Pvt. Ltd., Hyderabad 500082, India
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/62/1/22/pdf
Reference14 articles.
1. Zeng, D., Lin, Z., Yan, X., Liu, Y., Wang, F., and Tang, B. (2022, January 18–24). Face2exp: Combating data biases for facial expression recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.
2. Li, S., and Deng, W. (2018). Deep facial expression recognition: A survey. arXiv.
3. Facial expression recognition for monitoring neurological disorders based on convolutional neural network;Yolcu;Multimed. Tools Appl.,2019
4. Occlusion Aware Facial Expression Recognition Using CNN with Attention Mechanism;Li;IEEE Trans. Image Process.,2019
5. Zahara, L., Musa, P., Wibowo, E.P., Karim, I., and Musa, S.B. (2020, January 3–4). The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of MicroExpressions Face Using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi. Proceedings of the 2020 Fifth International Conference on Informatics and Computing (ICIC), Gorontalo, Indonesia.
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