Abstract
Micro-expressions (MEs) have the characteristics of small motion amplitude and short duration. How to learn discriminative ME features is a key issue in ME recognition. Motivated by the success of PCB model in person retrieval, this paper proposes a ME recognition method called PCB-PCANet+. Considering that the important information of MEs is mainly concentrated in a few key facial areas like eyebrows and eyes, based on the output of shallow PCANet+, we use a multiple branch LSTM networks to separately learn the local spatio-temporal features for each facial ROI region. In addition, in the stage of multiple branch fusion, we design a feature weighting strategy according to the significances of different facial regions to further improve the performances of ME recognition. The experimental results on the SMIC and CASME II datasets validate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pfister, T., Li, X., Zhao, G., Pietikäinen, M.: Recognising spontaneous facial micro-expressions. In: Proceedings of International Conference on Computer Vision, pp. 1449–1456 (2011)
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)
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikäinen, M.: A spontaneous micro-expression database: Inducement, collection and baseline. In: Proceedings of 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–6 (2013)
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: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9003, pp. 525–537. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16865-4_34
Zong, Y., Huang, X., Zheng, W., Cui, Z., Zhao, G.: Learning from hierarchical spatiotemporal descriptors for micro-expression recognition. IEEE Trans. Multimedia 20(11), 3160–3172 (2018)
Verma, M., Vipparthi, S.K., Singh, G., Murala, S.: Learnet: dynamic imaging network for micro expression recognition. IEEE Trans. Image Process. 29, 1618–1627 (2020)
Bilen, H., Fernando, B., Gavves, E., Vedaldi, A.: Action recognition with dynamic image networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2799–2813 (2018)
Thuseethan, S., Rajasegarar, S., Yearwood, J.: Deep3DCANN: a deep 3DCNN-ANN framework for spontaneous micro-expression recognition. Inf. Sci. 630, 341–355 (2023)
Xu, S., Zhou, Z., Shang, J.: Asymmetric adversarial-based feature disentanglement learning for cross-database micro-expression recognition. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5342–5350 (2022)
Mayya, V., Pai, R.M., Manohara Pai, M.: Combining temporal interpolation and DCNN for faster recognition of micro-expressions in video sequences. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 699–703 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
**a, B., Wang, S.: Micro-expression recognition enhanced by macro-expression from spatial-temporal domain. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1186–1193 (2021)
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp. 480–496 (2018)
Yan, W.J., et al.: CASME II: an improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE 9(1), 1–8 (2014)
Huang, X., Wang, S., Zhao, G., Piteikainen, M.: Facial micro-expression recognition using spatiotemporal local binary pattern with integral projection. In: Proceedings of IEEE International Conference on Computer Vision Works, Los Alamitos, CA, USA, pp. 1–9 (2015)
Patel, D., Hong, X., Zhao, G.: Selective deep features for micro-expression recognition. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2258–2263 (2016)
Li, J., Wang, Y., See, J., Liu, W.: Micro-expression recognition based on 3d flow convolutional neural network. Pattern Analysis and Applications 22(4), 1331–1339 (2019)
Zhou, L., Mao, Q., Huang, X., Zhang, F., Zhang, Z.: Feature refinement: an expression-specific feature learning and fusion method for micro-expression recognition. Pattern Recogn. 122, 108275 (2022)
Wang, S., Guan, S., Lin, H., Huang, J., Long, F., Yao, J.: Micro-expression recognition based on optical flow and pcanet+. Sensors 22(11), 4296 (2022)
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 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, S., Long, F., Yao, J. (2024). Micro-expression Recognition Based on PCB-PCANet+. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14452. Springer, Singapore. https://doi.org/10.1007/978-981-99-8076-5_13
Download citation
DOI: https://doi.org/10.1007/978-981-99-8076-5_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8075-8
Online ISBN: 978-981-99-8076-5
eBook Packages: Computer ScienceComputer Science (R0)