Subspace Learning and Joint Distribution Adaptation for Unsupervised Cross-Database Microexpression Recognition

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.