Zhaoqiang Xia
ABSTRACT
Micro-expression recognition (MER) is attracting more and more interests as it has important applications for analyzing human behaviors. Since the recognition ability for individual datasets has been improved greatly, few works have been devoted to the cross database task of MER, which is more challenging for capturing the subtle changes of micro-expressions from different environments. In this paper, we employ an end-to-end deep model for learning the representation and classifier automatically. In the deep model, the recurrent convolutional layers are utilized to exploit the learning ability with the optical flow fields of micro-expression sequences, which are enhanced by a motion magnification procedure. To ease the influence of samples from different datasets (environments), we present three normalization methods (i.e., sample-wise, subject-wise and dataset-wise methods) to restrain the variations of samples. The experiments are performed on the cross database of MERC2019 challenge, and achieve comparative performance than the baseline method.
- A. K. Davison, C. Lansley, N. Costen, K. Tan, and M. H. Yap. SAMM: A spontaneous micro-facial movement dataset. IEEE Transactions on Affective Computing, 9(1):116--129, 2018. Google Scholar
Digital Library
- A. K. Davison, M. H. Yap, N. Costen, K. Tan, C. Lansley, and D. Leightley. Micro-facial movements: An investigation on spatio-temporal descriptors. In European Conference on Computer Vision (ECCV), pages 111--123, 2014.Google Scholar
- X. Huang, S. J. Wang, X. Liu, G. Zhao, X. Feng, and M. Pietikäinen. Discriminative spatiotemporal local binary pattern with revisited integral projection for spontaneous facial micro-expression recognition. IEEE Transactions on Affective Computing, PP(99):1--1, 2017. Google ScholarDigital Library
- X. Huang, G. Zhao, X. Hong, W. Zheng, and M. Pietikainen. Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing, 175:564--578, 2016. Google ScholarDigital Library
- X. Li, T. Pfister, X. Huang, G. Zhao, and M. Pietikainen. A spontaneous micro-expression database: Inducement, collection and baseline. In IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pages 1--6. IEEE, 2013.Google ScholarCross Ref
- S. T. Liong, J. See, C. W. Phan, and K. S. Wong. Less is more: Micro-expression recognition from video using apex frame. Signal Processing: Image Communication, 62:82--92, 2018.Google ScholarCross Ref
- Y. J. Liu, J. K. Zhang, W. J. Yan, S. J. Wang, G. Zhao, and X. Fu. A main directional mean optical flow feature for spontaneous micro-expression recognition. IEEE Transactions on Affective Computing, 7(4):299--310, 2016. Google ScholarDigital Library
- W. Merghani, A. K. Davison, and M. H. Yap. A review on facial micro-expressions analysis: Datasets, features and metrics. arXiv, 2018.Google Scholar
- T. Pfister, X. Li, G. Zhao, and M. Pietikainen. Recognising spontaneous facial micro-expressions. In International Conference on Computer Vision (ICCV), pages 1449--1456. IEEE, 2011. Google ScholarDigital Library
- M. Takalkar, M. Xu, Q. Wu, and Z. Chaczko. A survey: facial micro-expression recognition. Multimedia Tools & Applications, 77(15):1930119325, 2018. Google ScholarDigital Library
- M. A. Takalkar and M. Xu. Image based facial micro-expression recognition using deep learning on small datasets. In International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1--7, 2017.Google ScholarCross Ref
- A. Vedaldi and K. Lenc. Matconvnet - convolutional neural networks for matlab. In ACM Multimedia, pages 689--692. ACM, 2015. Google ScholarDigital Library
- S. J. Wang, S. Wu, X. Qian, J. Li, and X. Fu. A main directional maximal difference analysis for spotting facial movements from long-term videos. Neurocomputing, 230:382--389, 2017. Google ScholarDigital Library
- S. J. Wang, W. J. Yan, X. Li, and G. Zhao. Micro-expression recognition using color spaces. IEEE Transactions on Image Processing, 24(12):6034, 2015.Google ScholarDigital Library
- H. Y. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, and W. Freeman. Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics, 31(4):13--15, 2012. Google ScholarDigital Library
- Z. Xia, X. Feng, X. Hong, and G. Zhao. Spontaneous facial micro-expression recognition via deep convolutional network. In International Conference on Image Processing Theory Tools & Applications, pages 235--240, 2018.Google ScholarCross Ref
- Z. Xia, X. Feng, J. Peng, X. Peng, and G. Zhao. Spontaneous micro-expression spotting via geometric deformation modeling. Computer Vision & Image Understanding, 147:87--94, 2016. Google ScholarDigital Library
- Z. Xia, X. Hong, X. Gao, X. Feng, and G. Zhao. Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions. IEEE transactions on Multimedia, Online, 2019.Google Scholar
- Z. Xia, J. Lin, and X. Feng. Trademark image retrieval via transformation-invariant deep hashing. Journal of Visual Communication and Image Representation, 59:108--116, 2019.Google ScholarDigital Library
- F. Xu, J. Zhang, and J. Z. Wang. Microexpression identification and categorization using a facial dynamics map. IEEE Transactions on Affective Computing, 8(2):254--267, 2017.Google ScholarDigital Library
- W. J. Yan, X. Li, S. J. Wang, G. Zhao, Y. J. Liu, Y.-H. Chen, and X. Fu. CASME II: An improved spontaneous micro-expression database and the baseline evaluation. PloS one, 9(l):e86041, 2014.Google Scholar
- W. J. Yan, Q. Wu, J. Liang, Y.-H. Chen, and X. Fu. How fast are the leaked facial expressions: The duration of micro-expressions. Journal of Nonverbal Behavior, 37(4):217--230, 2013.Google ScholarCross Ref
- M. H. Yap, J. See, X. Hong, and S.-J. Wang. Facial micro-expressions grand challenge 2018 summary. In IEEE Conference on Automatic Face & Gesture Recognition (FG), pages 675--678, 2018.Google Scholar
- Y. Zong, X. Huang, W. Zheng, Z. Cui, and G. Zhao. Learning from hierarchical spatiotemporal descriptors for micro-expression recognition. IEEE Transactions on Multimedia, PP(99):1--1, 2018.Google Scholar
- Y. Zong, W. Zheng, X. Huang, J. Shi, and G. Zhao. Domain regeneration for cross-database micro-expression recognition. IEEE Transactions on Image Processing, 27(99): 1--1, 2018.Google Scholar
Index Terms
- Cross-database Micro-Expression Recognition with Deep Convolutional Networks
Recommendations
Cross-Database Micro-Expression Recognition: A Benchmark
ICMR '19: Proceedings of the 2019 on International Conference on Multimedia RetrievalCross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problems in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing ...
Micro-Expression Recognition with Expression-State Constrained Spatio-Temporal Feature Representations
MM '16: Proceedings of the 24th ACM international conference on MultimediaRecognizing spontaneous micro-expression in video sequences is a challenging problem. In this paper, we propose a new method of small scale spatio-temporal feature learning. The proposed learning method consists of two parts. First, the spatial features ...
AU-assisted Graph Attention Convolutional Network for Micro-Expression Recognition
MM '20: Proceedings of the 28th ACM International Conference on MultimediaMicro-expressions (MEs) are important clues for reflecting the real feelings of humans, and micro-expression recognition (MER) can thus be applied in various real-world applications. However, it is difficult to perceive and interpret MEs correctly. With ...
Comments