Abstract
Microexpression recognition has been widely favored by researchers due to its many potential applications, such as business negotiation and lie detection. Cross-database microexpression recognition is more challenging and attractive than normal microexpression recognition because the training and testing samples come from different databases. The ensuing challenge is that the feature distributions between training and testing samples differ too much. As a result, the performance of current well-performing microexpression recognition methods often fails to achieve the desired effect. In this paper, we overcome this problem by introducing Subspace Learning and Joint Distribution Adaptation (SLJDA) by projecting the source and target domains into the subspace and later reducing the distance between them and then minimizing the distance between the marginal and conditional probability distributions of the data between the source domain and the target domain. To evaluate its performance, a large number of cross-database experiments are performed in the SMIC database and CASMEII database. The experimental results show the superiority of the method compared with existing microexpression recognition methods.
Highlights
Microexpression is a special, weak facial expression that usually lasts only 1/25 to 1/5 seconds [1]
Microexpressions can show the true thoughts of a person’s heart. erefore, automatic microexpression recognition technology can be applied in many practical scenarios such as marital relationship prediction [3], clinical diagnosis, and teaching assessment [4, 5], and even in the future, it may be extended to communication security [6,7,8,9], intelligent devices, etc
With the development of intelligent technology, researchers have gradually focused their attention on it, and microexpression recognition has made some progress. Both training and testing samples for microexpression recognition come from the same microexpression database. is is not the case in practical applications, where the training and testing samples for microexpression recognition come from different databases
Summary
Microexpression is a special, weak facial expression that usually lasts only 1/25 to 1/5 seconds [1]. The classical work based on these methods includes Transfer Component Analysis (TCA) [13], which finds a feature mapping while retaining the original information of the source domain and the target domain, so that the conditional distribution between the two domains after the mapping is relatively close. Maximize the target domain variance samples, minimize the differences in the distribution of the corresponding subspaces of the projected source and target domains, and minimize the distances between subspaces to reduce the difference between source and target domains and improve the recognition of unsupervised cross-database microexpression.
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