Image-based Facial Emotion Detection SYSTEM

Authors

  • Cheng Zhang

DOI:

https://doi.org/10.54097/b02twk08

Keywords:

Facaial emtion; Object detection; Face detection

Abstract

This paper proposes an image-based approach for emotion reco2gnition, aiming to infer human emotional states through the analysis of facial expression images. Firstly, we introduce the research background and significance of emotion recognition technology, and review current mainstream methods for emotion recognition. Secondly, we provide a detailed description of the design and implementation process of the proposed emotion recognition system, including key steps such as data preprocessing, feature extraction, and model construction. In the experimental section, we conduct systematic performance evaluations and comparative experiments using publicly available datasets, validating the effectiveness and accuracy of the proposed method. The experimental results demonstrate that our approach achieves outstanding performance in emotion recognition tasks and exhibits strong generalization capability. Finally, we discuss the limitations of the proposed method, future research directions, as well as the potential value and challenges in real-world applications. Through this research, we contribute to the further development and application of image-based emotion recognition technology.

References

Ekman, P., & Friesen, W. V. (1978). Facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press.

Viola, P., & Jones, M. J. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 1, pp. I-511). IEEE.

Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720.

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks?. In Advances in Neural Information Processing Systems (pp. 3320-3328).

Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255).

Wang, Y., See, J., Phan, H. H., & Ng, A. K. (2018). Multimodal fusion with recurrent neural networks for emotion recognition in video sequences. IEEE Transactions on Affective Computing, 11(2), 230-244.

Zhang, Z., Song, Y., Qi, H., Cheng, L., Jiang, M., & Hu, B. (2019). Emotion recognition from facial expressions using multilevel attention CNN. IEEE Transactions on Affective Computing, 12(4), 819-833.

Li, X., Chen, Y., Li, Y., & Wu, D. (2020). Adversarial training for robust emotion recognition. IEEE Transactions on Information Forensics and Security, 15, 1993-2007.

Liu, P., Han, X., & Chen, C. (2020). Deep learning for emotion recognition: A comprehensive review. Neurocomputing, 415, 295-308.

Li, H., Chen, X., & Hu, Y. (2019). Deep learning for emotion recognition: A survey. Neurocomputing, 323, 3-22.

Zhang, Y., Song, X., & Wang, X. (2017). Facial expression recognition: A survey and real-world applications. Image and Vision Computing, 65, 1-14.

Zheng, W., Liu, H., & Lu, W. (2020). A comprehensive survey on emotion recognition: Progress from classical models to deep learning and benchmark datasets. arXiv preprint arXiv: 2008.04303.

Dhall, A., Goecke, R., & Lucey, S. (2014). Emotion recognition in the wild challenge 2014: Baseline, data and protocol. In Proceedings of the 16th ACM International Conference on Multimodal Interaction.

Liu, Y., Li, P., & Wang, H. (2021). Emotion recognition using deep learning: A review and future directions. Neurocomputing, 451, 26-39.

Zhang, J., Wang, Y., & Liu, Z. (2020). Multi-modal emotion recognition based on deep learning: A survey. Pattern Recognition, 107, 107521.

Sharma, A., & Singh, P. (2021). Emotion recognition in the wild using adversarial learning. IEEE Transactions on Affective Computing, 12(1), 26-39.

Chen, S., Li, X., & Zhu, S. (2021). Graph-based emotion recognition with self-supervised learning. Pattern Recognition Letters, 148, 1-8.

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Published

06-08-2024

Issue

Section

Articles

How to Cite

Zhang, C. (2024). Image-based Facial Emotion Detection SYSTEM. Computer Life, 12(2), 20-26. https://doi.org/10.54097/b02twk08