TKRM: Learning a Transfer Kernel Regression Model for Cross-Database Micro-Expression Recognition

Zixuan Chen, Cheng Lu, Feng Zhou and Yuan Zong ()
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Zixuan Chen: School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Cheng Lu: School of Biological Science and Medicial Engineering, Southeast University, Nanjing 210096, China
Feng Zhou: College of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China
Yuan Zong: School of Biological Science and Medicial Engineering, Southeast University, Nanjing 210096, China

Mathematics, 2023, vol. 11, issue 4, 1-12

Abstract: Cross-database micro-expression recognition (MER) is a more challenging task than the conventional one because its labeled training (source) and unlabeled testing (target) micro-expression (ME) samples are from different databases. In this circumstance, a large feature-distribution gap may exist between the source and target ME samples due to the different sample sources, which decreases the recognition performance of existing MER methods. In this paper, we focus on this challenging task by proposing a simple yet effective method called the transfer kernel regression model (TKRM). The basic idea of TKRM is to find an ME-discriminative, database-invariant and common reproduced kernel Hilbert space (RKHS) to bridge MEs belonging to different databases. For this purpose, TKRM has the ME discriminative ability of learning a kernel mapping operator to generate an RKHS and build the relationship between the kernelized ME features and labels in such RKHS. Meanwhile, an additional novel regularization term called target sample reconstruction (TSR) is also designed to benefit kernel mapping operator learning by improving the database-invariant ability of TKRM while preserving the ME-discriminative one. To evaluate the proposed TKRM method, we carried out extensive cross-database MER experiments on widely used micro-expression databases, including CASME II and SMIC. Experimental results obtained proved that the proposed TKRM method is indeed superior to recent state-of-the-art domain adaptation methods for cross-database MER.

Keywords: cross-database micro-expression recognition; micro-expression recognition; probability distribution gap; kernel learning; domain adaptation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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