Abstract
Micro-expressions are rapid and subtle facial movements that can reflect the most real emotional state hidden in the human heart. Classifying different micro-expressions is still challenging because of their short duration and low intensity. This paper proposes new neural network models, Simplified SE-DenseNet-cc and SE-ResNet-cc, incorporating Eulerian video magnification (EVM) to enlarge micro-expression movements. Important features can be selectively enhanced, and unimportant features can be compressed using SE-block. The experimental results show that our proposed methods perform better than most of the algorithms in CASME-II and SMIC.
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References
Darwin, C.: The expression of the emotions in man and animals: frontmatter. Portable Darwin. 123(1), 146–147 (2013)
Porter, S., Brinke, L.: Reading between the lies. Psychol. Sci. 19, 508–514 (2008). https://doi.org/10.1111/j.1467-9280.2008.02116.x
Ekman, P., Rosenberg, E., (eds.) What the face reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS), 2nd ed., Oxford University Press (2012). https://doi.org/10.1093/acprof:oso/9780195179644.001.0001
Weinberger, S.: Airport security: intent to deceive? Nature 465, 412–415 (2010). https://doi.org/10.1038/465412a0
Ekman, P.: Telling lies clues to deceit in the marketplace, politics, and marriage. W W Norton & Co. (1991)
Yan, W., et al.: CASME II: an improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE 9(1), 1–8 (2014). https://doi.org/10.1371/journal.pone.0086041
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikäinen, M.: A spontaneous microexpression database: inducement, collection and baseline. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, 2013, pp. 1–6 (2013). https://doi.org/10.1109/FG.2013.6553717
Ben, X., et al.: Video-based facial micro-expression analysis: a survey of datasets, features and algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5826–5846 (2022). https://doi.org/10.1109/TPAMI.2021.3067464
Davison, A., Lansley, C., Costen, N., Tan, K., Yap, M.H.: SAMM: a spontaneous micro-facial movement dataset. IEEE Trans. Affect. Comput. 9(1), 116–129 (2018). https://doi.org/10.1109/TAFFC.2016.2573832
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007). https://doi.org/10.1109/TPAMI.2007.1110
Yao, S., He, N., Zhang, H., Yoshie, O.: Micro-expression recognition by feature points tracking. In: International Conference on Communications, Bucharest, Romania, pp. 1–4 (2014). https://doi.org/10.1109/ICComm.2014.6866671
Li, Y., Huang, X., Zhao, G.: Can micro-expression be recognized based on single apex frame? In: 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, pp. 3094–3098 (2018). https://doi.org/10.1109/ICIP.2018.8451376
Cai, L., Li, H., Dong, W., Fang, H.: Micro-expression recognition using 3D DenseNet fused squeeze-and-excitation networks. Appl. Soft Comput. 119, 108594 (2022)
Wu, H., Rubinstein, M., Shih, E., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 1–8 (2012). https://doi.org/10.1145/2185520.2185561
Howard, A., et al.: Searching for MobileNetV3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 1314–1324 (2019). https://doi.org/10.1109/ICCV.2019.00140
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745
Yao, L., Xiao, X., Cao, R., Chen, F., Chen, T.: Three stream 3D CNN with SE block for micro-expression recognition. In: 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China, pp. 439–443 (2020). https://doi.org/10.1109/ICCEA50009.2020.00101
Wang, C.Y., Peng, M., Bi, T., Chen, T.: Micro-attention for micro-expression recognition. Neurocomputing 410, 354–362 (2020)
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Chen, X., Nishiyama, M., Iwai, Y. (2024). Comparison of Simplified SE-ResNet and SE-DenseNet for Micro-Expression Classification. In: Yan, W.Q., Nguyen, M., Nand, P., Li, X. (eds) Image and Video Technology. PSIVT 2023. Lecture Notes in Computer Science, vol 14403. Springer, Singapore. https://doi.org/10.1007/978-981-97-0376-0_26
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DOI: https://doi.org/10.1007/978-981-97-0376-0_26
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