Unsupervised cross-database micro-expression recognition based on distribution adaptation

Different from the traditional macro-expressions, micro-expressions are unconscious, quick and trustworthy facial expressions, which can reveal real emotion. Micro-expressions can provide information that is important and crucial in applications such as lie detection, criminal investigation, pain or mood assessment, etc. However, it is worth noting that most current micro-expression recognition methods rely only on a single micro-expression database. If the training and test samples belong to different domains, for example, different micro-expression databases, the accuracy of existing micro-expression recognition methods will decrease dramatically. To solve this problem, we propose an unsupervised cross-database micro-expression recognition method based on distribution adaptation. Compared with most advanced unsupervised cross-database recognition methods, the proposed method has better performance on micro-expression recognition tasks.