T. Zhang et al., "Cross-Database Micro-Expression Recognition: A Benchmark," in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 544-559, 1 Feb. 2022, doi: 10.1109/TKDE.2020.2985365
Cross-database micro-expression recognition : a benchmark
|Author:||Zhang, Tong1; Zong, Yuan2; Zheng, Wenming2;|
1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
3University, Xi’an 710049, China
4School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
5Center for Machine Vision and Signal Analysis, University of Oulu, 90016 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023033134145
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-31
Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing samples in CDMER come from different micro-expression databases, resulting in inconsistency of the feature distributions between the training and testing sets. In this paper, we contribute to this topic from three aspects. First, we establish a CDMER experimental evaluation protocol aiming to allow the researchers to conveniently work on this topic and evaluate their proposed methods under the same standard. Second, we conduct benchmark experiments by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for investigating CDMER problem from two different perspectives. Third, we propose a novel DA method called region selective transfer regression (RSTR) to deal with the CDMER task. The overall superior performance of RSTR over the state-of-the-art DA methods demonstrates that taking into consideration the facial local region information used in RSTR contributes to developing effective DA methods for dealing with CDMER problem.
IEEE transactions on knowledge and data engineering
|Pages:||544 - 559|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the National Key Research and Development Program of China under Grants 2018YFB1305200, 2019YFA0706200 and 2019YFB1703600, in part by the National Natural Science Foundation of China under Grants 61921004, 61751202, U1813203, 61702195, U1801262, 61902064, and 81971282, in part by the Fundamental Research Funds for the Central Universities under Grant 2242018K3DN01, Grant 2242019K40047, and 2242020K40079, in part by the Academy of Finland, in part by the Tekes Fidipro Program, and in part by the Infotech Oulu.
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