Cross-Database Micro-Expression Recognition: A Benchmark

Abstract

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: http://aip.seu.edu.cn/cdmer.