Abstract
Facial micro-expression (FME) is a fast and subtle facial muscle movement that typically reflects person’s real mental state. It is a huge challenge in the FME recognition task due to the low intensity and short duration. FME can be decomposed into a combination of facial muscle action units (AU), and analyzing the correlation between AUs is a solution for FME recognition. In this paper, we propose a framework called spatio-temporal AU graph convolutional network (STA-GCN) for FME recognition. Firstly, pre-divided AU-related regions are input into the 3D CNN, and inter-frame relations are encoded by inserting a Non-Local module for focusing on apex information. Moreover, to obtain the inter-AU dependencies, we construct separate graphs of their spatial relationships and activation probabilities. The relationship feature we obtain from the graph convolution network (GCN) are used to activate on the full-face features. Our proposed algorithm achieves state-of-the-art accuracy of 76.08% accuracy and F1-score of 70.96% on the CASME II dataset, which outperformance all baselines.
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References
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 (2016)
Ekman, R.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression using the Facial Action Coding System (FACS). Oxford University Press, USA (1997)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Khor, H.Q., See, J., Phan, R.C.W., Lin, W.: Enriched long-term recurrent convolutional network for facial micro-expression recognition. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 667–674. IEEE (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Liu, Y.J., Zhang, J.K., Yan, W.J., Wang, S.J., Zhao, G., Fu, X.: A main directional mean optical flow feature for spontaneous micro-expression recognition. IEEE Trans. Affect. Comput. 7(4), 299–310 (2015)
Liu, Z., Dong, J., Zhang, C., Wang, L., Dang, J.: Relation modeling with graph convolutional networks for facial action unit detection. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 489–501. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_40
Ma, C., Chen, L., Yong, J.: Au R-CNN: encoding expert prior knowledge into R-CNN for action unit detection. Neurocomputing 355, 35–47 (2019)
Pfister, T., Li, X., Zhao, G., Pietikäinen, M.: Recognising spontaneous facial micro-expressions. In: 2011 International Conference on Computer Vision, pp. 1449–1456. IEEE (2011)
Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)
Reddy, S.P.T., Karri, S.T., Dubey, S.R., Mukherjee, S.: Spontaneous facial micro-expression recognition using 3D spatiotemporal convolutional neural networks. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
Shi, X., Yang, C., Xia, X., Chai, X.: Deep cross-species feature learning for animal face recognition via residual interspecies equivariant network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 667–682. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58583-9_40
Verma, M., Vipparthi, S.K., Singh, G., Murala, S.: Learnet: dynamic imaging network for micro expression recognition. IEEE Trans. Image Process. 29, 1618–1627 (2019)
Wang, S.J., et al.: Micro-expression recognition with small sample size by transferring long-term convolutional neural network. Neurocomputing 312, 251–262 (2018)
Wang, S.J., et al.: Micro-expression recognition using color spaces. IEEE Trans. Image Process. 24(12), 6034–6047 (2015)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Shi, X., Yang, C., Xia, X., Chai, X.: Deep cross-species feature learning for animal face recognition via residual interspecies equivariant network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 667–682. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58583-9_40
Wang, Y., See, J., Phan, R.C.W., Oh, Y.H.: Lbp with six intersection points: reducing redundant information in lbp-top for micro-expression recognition. In: Asian Conference on Computer Vision, pp. 525–537. Springer (2014)
Xia, Z., Hong, X., Gao, X., Feng, X., Zhao, G.: Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions. IEEE Trans. Multimed. 22(3), 626–640 (2019)
Xu, M., Zhao, C., Rojas, D.S., Thabet, A., Ghanem, B.: G-tad: Sub-graph localization for temporal action detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10156–10165 (2020)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Yan, W.J., et al.: Casme ii: an improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE 9(1), e86041 (2014)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Zhang, Y., et al.: Joint representation and estimator learning for facial action unit intensity estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3457–3466 (2019)
Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: European Conference on Computer Vision, pp. 94–108. Springer (2014)
Zhao, G., Pietikainen, 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)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. U20B2062), the fellowship of China Postdoctoral Science Foundation (No. 2021M690354), the Beijing Municipal Science & Technology Project (No. Z191100007419001).
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Zhao, X., Ma, H., Wang, R. (2021). STA-GCN: Spatio-Temporal AU Graph Convolution Network for Facial Micro-expression Recognition. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_7
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