University of Oulu

Y. Zong, W. Zheng, X. Huang, J. Shi, Z. Cui and G. Zhao, "Domain Regeneration for Cross-Database Micro-Expression Recognition," in IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2484-2498, May 2018. doi: 10.1109/TIP.2018.2797479

Domain regeneration for cross-database micro-expression recognition

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Author: Zong, Yuan1,2; Zheng, Wenming3; Huang, Xiaohua2;
Organizations: 1Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
2Center for Machine Vision and Signal Analysis, Faulty of Information Technology and Electrical Engineering, University of Oulu, FI-90014 Oulu, Finland
3Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
4School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 6.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-04-05


Recently, micro-expression recognition has attracted lots of researchers’ attention due to its potential value in many practical applications, e.g., lie detection. In this paper, we investigate an interesting and challenging problem in micro-expression recognition, i.e., cross-database micro-expression recognition, in which the training and testing samples come from different micro-expression databases. Under this problem setting, the consistent feature distribution between the training and testing samples originally existing in conventional micro-expression recognition would be seriously broken, and hence, the performance of most current well-performing micro-expression recognition methods may sharply drop. In order to overcome it, we propose a simple yet effective framework called domain regeneration (DR) in this paper. The DR framework aims at learning a domain regenerator to regenerate the micro-expression samples from source and target databases, respectively, such that they can abide by the same or similar feature distributions. Thus, we are able to use the classifier learned based on the labeled source micro-expression samples to predict the label information of the unlabeled target micro-expression samples. To evaluate the proposed DR framework, we conduct extensive cross-database micro-expression recognition experiments designed based on the Spontaneous Micro-Expression Database and Chinese Academy of Sciences Micro-Expression II Database. Experimental results show that compared with the recent state-of-the-art cross-database emotion recognition methods, the proposed DR framework has more promising performance.

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Series: IEEE transactions on image processing
ISSN: 1057-7149
ISSN-E: 1941-0042
ISSN-L: 1057-7149
Volume: 27
Issue: 5
Pages: 2484 - 2498
DOI: 10.1109/TIP.2018.2797479
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, in part by the National Natural Science Foundation of China under Grant 61231002, Grant 61572009, Grant 61772276, and Grant 61602244, in part by the China Scholarship Council, in part by the Scientific Research Foundation of Graduate School, Southeast University under Grant YBJJ1774, in part by the Academy of Finland, in part by the Tekes Fidipro Program, in part by the Strategic Funds of the University of Oulu, Finland, in part by Infotech Oulu, in part by the Jorma Ollila Grant of Nokia Foundation, and in part by the Central Fund Grant of Finnish Cultural Foundation.
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