Cross-dataset micro-expression identification based on facial ROIs contribution quantification

In this work, we propose a novel cross-dataset micro-expression identification method based on facial regions of interest contribution quantification, where the training samples are from the source dataset and the test samples are from the target dataset. Specifically, a micro-expression video clip (i.e., a sample) is first sampled at intervals to capture an image sequence consisting of multiple consecutive video frames. Then, locate and crop the facial area in the image frame, and perform facial landmark detection on the cropped area. Next calculate the optical-flow field of the image sequence, and extract the improved Main Directional Mean Optical-flow feature. Finally, the learned group sparse model is used as the classifier to predict the label information of the unmarked target samples, so as to complete the category recognition of micro-expressions. Extensive cross-dataset comparative experiments on four spontaneous micro-expression datasets, CASME, CASME II, SMIC-HS, and SAMM, show that the recognition strategy proposed in this work is effective. Compared with several other advanced recognition methods, our method has better cross-dataset recognition performance, not only the classification accuracy is higher, but also the classification stability is better when faced with different target datasets and different micro-expression categories. It can be applied in many fields such as clinical diagnosis, interrogation, negotiation, and national security.