Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, and Guoying Zhao. 2017. Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition. In Proceedings of the 25th ACM international conference on Multimedia (MM '17). ACM, New York, NY, USA, 872-880. DOI: https://doi.org/10.1145/3123266.3123367
Learning a target sample re-generator for cross-database micro-expression recognition
|Author:||Zong, Yuan1; Huang, Xiaohua2; Zheng, Wenming1;|
1Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University Nanjing 210096, China
2Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu Oulu FI-90014, Finland
3School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing 210094, China
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019060719451
Association for Computing Machinery,
|Publish Date:|| 2019-06-07
In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.
|Pages:||872 - 890|
MM '17 Proceedings of the 2017 ACM on Multimedia Conference : Mountain View, California, USA - October 23 - 27, 2017
ACM on Multimedia Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work was supported by the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61231002 and Grant 61572009, China Scholarship Council, Academy of Finland, Tekes Fidipro Program and Infotech Oulu. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MM '17 Proceedings of the 2017 ACM on Multimedia Conference : Mountain View, California, USA - October 23 - 27, 2017, https://doi.org/10.1145/3123266.3123367.