Facial expression classification
DOI:
https://doi.org/10.54097/hset.v41i.6741Keywords:
facial expression recognition; Mini-Xception; OpenCV.Abstract
At present, emotion classification has become a hot topic in artificial intelligence pattern recognition. The facial expression recognition (FER) is indispensable for computers to understand the emotional information conveyed by expressions. In the past, using traditional features to extract and classify facial expressions has not achieved satisfactory accuracy, so the classification of facial emotions is still a challenge. The model used in the paper is an existing one - MINI_XCEPTION, the dominant framework for CNNs that extracts features from images to identify and classify seven facial emotions. The model was trained on a dataset of people’s facial expressions (Kaggle). This model has a significant improvement over the previous model.
Downloads
References
N. Jain, S. Kumar, A. Kumar, P. Shamsolmoali, and M. Zareapoor. Hybrid deep neural networks for face emotion recognition. Pattern Recognition Letters, 115: 101–106, 2018. Multimodal Fusion for Pattern Recognition. 1, 3
S. A. Hussain and A. S. A. A. Balushi. A real time face emotion classification and recognition using deep learning model. Journal of Physics: Conference Series, 1432(1): 012087, jan 2020. 1
P. Ekman, W. V. Friesen. Constants across cultures in the face and emotion [J]. Journal of Personality and Social Psychology, 1971, 17(2): 124-129.
P. Viola, M. J. Jones. Robust Real-Time Face Detection [J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
C. Szegedy, W. Liu, Y. Q. Jia, et al. “Going deeper with convolutions.”Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
Chollet F. “Xception: Deep learning with depthwise separable convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
M. Wang, B. Liu, H. Foroosh. “Design of Efficient Convolutional Layers using Single Intra-channel Convolution”, Topological Subdivisioning and Spatial “Bottleneck” Structure [EB/OL]. [2020-03-15].
I. J. Goodfellow, D. Erhan, C. P. Luc, et al. “Challenges in representation learning: a report on three machine learning contests [J]. Neural Networks”, 2015(64): 59-63.
D. P. Kingma, J. L. Ba. Adam: A method for stochastic optimization [J]. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015: 1-15.
I. Song, H. Kim, and P. B. Jeon. Deep learning for real-time robust facial expression recognition on a smartphone. In 2014 IEEE International Conference on Consumer Electronics (ICCE), pages 564–567, 2014. 2
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
