Cross-Database Micro-Expression Recognition

Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problems 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 two aspects. First, we establish a CDMER experimental evaluation protocol and provide a standard platform for evaluating their proposed methods. Second, we conduct extensive benchmark experiments by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for investigating the CDMER problem from two different perspectives and deeply analyze and discuss the experimental results. In addition, all the data and codes involving CDMER in this paper are released on our project website:

Zong Yuan, Zheng Wenming, Hong Xiaopeng, Tang Chuangao, Cui Zhen, Zhao Guoying

Publication type:
A4 Article in conference proceedings

Place of publication:
Proceeding ICMR ’19 Proceedings of the 2019 on International Conference on Multimedia Retrieval

cross-database micro-expression recognition, domain adaptation, Micro-expression Recognition, spatiotemporal descriptors, transfer learning


Full citation:
Yuan Zong, Wenming Zheng, Xiaopeng Hong, Chuangao Tang, Zhen Cui, and Guoying Zhao. 2019. Cross-Database Micro-Expression Recognition: A Benchmark. In Proceedings of the 2019 on International Conference on Multimedia Retrieval (ICMR ’19). Association for Computing Machinery, New York, NY, USA, 354–363. DOI:


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