  In this paper, we investigate the cross-database micro-expression recognition
problem, where the training and testing samples are from two different
micro-expression databases. Under this setting, the training and testing
samples would have different feature distributions and hence the performance of
most existing micro-expression recognition methods may decrease greatly. To
solve this problem, we propose a simple yet effective method called Target
Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to
re-generate the samples from target micro-expression database and the
re-generated target samples would share same or similar feature distributions
with the original source samples. For this reason, we can then use the
classifier learned based on the labeled source samples to accurately predict
the micro-expression categories of the unlabeled target samples. To evaluate
the performance of the proposed TSRG method, extensive cross-database
micro-expression recognition experiments designed based on SMIC and CASME II
databases are conducted. Compared with recent state-of-the-art cross-database
emotion recognition methods, the proposed TSRG achieves more promising results.
