期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 34, 期 2, 页码 544-559出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2020.2985365
关键词
Databases; Emotion recognition; Protocols; Training; Cameras; Feature extraction; Task analysis; Cross-database micro-expression recognition; micro-expression recognition; domain adaptation; transfer learning; spatiotemporal descriptors
类别
资金
- National Key Research and Development Program of China [2018YFB1305200, 2019YFA0706200, 2019YFB1703600]
- National Natural Science Foundation of China [61921004, 61751202, U1813203, 61702195, U1801262, 61902064, 81971282]
- Fundamental Research Funds for the Central Universities [2242018K3DN01, 2242019K40047, 2242020K40079]
- Academy of Finland
- Tekes Fidipro Program
- Infotech Oulu
This paper discusses the challenges and importance of cross-database micro-expression recognition (CDMER) and contributes to this field by establishing an evaluation protocol, conducting benchmark experiments, and proposing a novel DA method.
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.
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