The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Micro-expression recognition based on 3D flow convolutional neural network
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation
Yan, W.-J., Li, X., Wang, S.-J., Zhao, G., Liu, Y.-J., Chen, Y.-H., & Fu, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086041 · Cited by 993
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition
Jung, H., Lee, S., Yim, J., Park, S., & Kim, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.341 · Cited by 781 · cited 3×
“Subsequent works by [15] and [13] proposed models that jointly learn from two types of data, i.”
BackgroundMicromomentary facial expressions as indicators of ego mechanisms in psychotherapy
Haggard, E. A., & Isaacs, K. S. (1966) · Methods of Research in Psychotherapy · doi:10.1007/978-1-4684-6045-2_14 · Cited by 343
Cited by
OFF-ApexNet on micro-expression recognition system
Gan, Y. S., Liong, S.-T., Yau, W.-C., Huang, Y.-C., & Tan, L.-K. (2019) · Signal Processing: Image Communication · doi:10.1016/j.image.2019.02.005 · Cited by 303
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
Liong, S.-T., Gan, Y. S., See, J., Khor, H.-Q., & Huang, Y.-C. (2019) · 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) · doi:10.1109/fg.2019.8756567 · Cited by 253
Self-citationRecognizing Facial Expressions Using a Shallow Convolutional Neural Network
Miao, S., Xu, H., Han, Z., & Zhu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2921220 · Cited by 110
Metadata
- Authors
- Li, J., Wang, Y., See, J., & Liu, W.
- Venue
- Pattern Analysis and Applications
- Published
- 2018-11-08
- Volume
- 22
- Issue
- 4
- Pages
- 1331-1339
- ISSN
- 1433-7541 (ISSN-L)
- 1433-755X
- OpenAlex
- W2900180747
- Cited by
- 173
- References
- 7
- FWCI
- 15.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Li, J., Wang, Y., See, J., & Liu, W. (2019). Micro-expression recognition based on 3D flow convolutional neural network. Pattern Analysis and Applications. https://doi.org/10.1007/s10044-018-0757-5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
