Cross-Database Micro-Expression Recognition: A Benchmark

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 2; pp. 544 - 559
• Main Authors: Zhang, Tong, Zong, Yuan, Zheng, Wenming, Chen, C. L. Philip, Hong, Xiaopeng, Tang, Chuangao, Cui, Zhen, Zhao, Guoying
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmarks; Cameras; Cross-database micro-expression recognition; domain adaptation; Emotion recognition; Feature extraction; micro-expression recognition; Protocols; Recognition; spatiotemporal descriptors; Task analysis; Training; transfer learning
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2020.2985365

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.