Application of Noise Detection Using Confidence Learning in Lightweight Expression Recognition System
Source abstract
Abstract
Facial expression is an important carrier to reflect psychological emotion, and the lightweight expression recognition system with small-scale and high transportability is the basis of emotional interaction technology of intelligent robots. With the rapid development of deep learning, fine-grained expression classification based on the convolutional neural network has strong data-driven properties, and the quality of data has an important impact on the performance of the model. To solve the problem that the model has a strong dependence on the training dataset and weak generalization performance in real environments in a lightweight expression recognition system, an application method of confidence learning is proposed. The method modifies self-confidence and introduces two hyper-parameters to adjust the noise of the facial expression datasets. A lightweight model structure combining a deep separation convolution network and attention mechanism is adopted for noise detection and expression recognition. The effectiveness of dynamic noise detection is verified on datasets with different noise ratios. Optimization and model training is carried out on four public expression datasets, and the accuracy is improved by 4.41% on average in multiple test sample sets. A lightweight expression recognition system is developed, and the accuracy is significantly improved, which verifies the effectiveness of the application method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Emotion and Mood Recognition · Advanced Technology in Applications · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
Jiangsu Key Lab of Remote Measurement and Control, Southeast University, Nanjing 210096, China
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Funding
Special project of Jiangsu basic research plan (cutting-edge leading technology)
References (36)
Show source-provided references
1. Communication without words
2. Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses10.1016/j.imavis.2013.12.007 ↗
3. Summary of facial expression recognition methods based on image
4. Tang, Y. (2015). Deep Learning Using Linear Support Vector Machines. arXiv.
5. Nearest Neighbor Pattern Classification10.1109/TIT.1967.1053964 ↗
6. Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex10.1113/jphysiol.1962.sp006837 ↗
7. ImageNet Classification with Deep Convolutional Neural Networks10.1145/3065386 ↗
8. Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.
9. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
10. He, K., Zhang, X., Ren, S., and Sun, J. (July, January 6). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
11. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
12. Yang, H.T. (2020). Facial Expression Recognition Method Based on Lightweight Convolutional Neural Network. [Master’s Thesis, Beijing University of Civil Engineering and Architecture].
13. Universal and cultural differences in facial expression of emotion10.1037/0022-3514.53.4.712 ↗
14. Wang, K., Peng, X., Yang, J., Lu, S., and Qiao, Y. (2020, January 16–18). Suppressing Uncertainties for Large-Scale Facial Expression Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00693 ↗
15. Confident learning: Estimating uncertainty in dataset labels10.1613/jair.1.12125 ↗
16. Xiao, H., Li, W., Zeng, G., Wu, Y., Xue, J., Zhang, J., Li, C., and Guo, G. (2022). On-Road Driver Emotion Recognition Using Facial Expression. Appl. Sci., 12.10.3390/app12020807 ↗
17. Research on real-time interaction for the emotion recognition robot based on deepwise separable convolution
18. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
19. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
20. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–22). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
21. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and <0.5 MB Model Size. arXiv.
22. Ioffe, S., and Szegedy, C. (2015, January 7–9). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the Machine Learning Research, Lille, France.
23. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR.2016.308 ↗
24. Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.10.1609/aaai.v31i1.11231 ↗
25. Research on Knowledge Base Error Detection Method Based on Confidence Learning
26. Handling label noise in Air Traffic Complexity Evaluation Based on Confidence Learning and XGBoost
27. Hyper-parameter optimization of CNN based on improved Bayesian optimization algorithm
28. Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks10.1109/LSP.2016.2603342 ↗
29. Chollet, F. (2017, January 21–26). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.195 ↗
30. Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., and Bengio, Y. (2013, January 3–7). Challenges in representation learning: A report on three machine learning contests. Proceedings of the International Conference on Neural Information Processing, Daegu, Korea.10.1007/978-3-642-42051-1_16 ↗
31. Barsoum, E., Zhang, C., Ferrer, C.C., and Zhang, Z. (2016, January 12–16). Training deep networks for facial expression recognition with crowd-sourced label distribution. Proceedings of the 18th ACM International Conference on Multimodal Interaction, Tokyo, Japan.10.1145/2993148.2993165 ↗
32. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., and Matthews, I. (2010, January 13–18). The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, San Francisco, CA, USA.10.1109/CVPRW.2010.5543262 ↗
33. Li, S., Deng, W., and Du, J. (2017, January 21–26). Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild. Proceedings of the IEEE Conference On computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.277 ↗
34. Lundqvist, D., Flykt, A., and Öhman, A. (1998). Karolinska directed emotional faces. Cogn. Emot.10.1037/t27732-000 ↗
35. Lyons, M., Akamatsu, S., Kamachi, M., and Gyoba, J. (1998, January 14–16). Coding facial expressions with Gabor wavelets. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan.
36. Emotion EEG recognition based on the adaptive optimized spatial-frequency differential entropy