Facial recognition has now played a pivotal role in many applications, including biomechanics, sports, image segment, animation, and robotics, etc. Although commercial facial recognition is matured, micro-expression recognition is still in its infancy and has attracted more attention from researchers in recent years. Usually, test and training samples can be recorded by different equipment throughout a variety of conditions, or by heterologous species. As a result, it is necessary to investigate whether the common micro-expression recognition algorithm is still feasible when the test samples are different from the training samples. In the present study, a series of well-developed algorithms for multi-source domain adaptation, the basic principles of multi-source domain adaptation, and the feature representation method has been discussed. A new method called the novel super-wide regression network (SWiRN) model has been introduced. Finally, some loss functions that are commonly used in neural networks for multiple source domain adaptations have been presented.