Cross-Database Micro-Expression Recognition: A Benchmark
Cross-Database Micro-Expression Recognition: A Benchmark
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DOI：
10.1109/tkde.2020.2985365
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发表时间：
2020-04
影响因子：
8.9
通讯作者：
Tong Zhang;Yuan Zong;Wenming Zheng;C. L. Philip Chen;Xiaopeng Hong;Chuangao Tang;Zhen Cui;Guoying Zhao
中科院分区：
文献类型：
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作者：
Tong Zhang;Yuan Zong;Wenming Zheng;C. L. Philip Chen;Xiaopeng Hong;Chuangao Tang;Zhen Cui;Guoying Zhao
跨数据库微表情识别（CDMER）是近年来微表情分析中一个新兴的热点问题。由于CDMER中的训练样本和测试样本来自不同的微表情数据库，导致训练集和测试集之间的特征分布不一致，因此与传统的微表情识别相比，CDMER更具挑战性。在本文中，我们从三个方面对这一主题进行了探讨。首先，我们建立了一个CDMER实验评估方案，旨在使研究人员能够方便地开展该主题的工作，并在相同的标准下评估他们提出的方法。其次，我们利用9种最先进的领域自适应方法和6种流行的时空描述符进行基准实验，从两个不同的角度研究CDMER问题。第三，我们提出了一种新的数据处理方法，称为区域选择性转移回归（RSTR）来处理CDMER任务。RSTR的总体性能优于现有的数据分析方法，这表明在RSTR中考虑人脸局部区域信息有助于开发有效的数据分析方法来处理CDMER问题。
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.