Automatic, continuous, non-invasive detection of pain and it’s intensity in clinical settings is needed to assess and manage pain. Therefore, a solution is proposed which deals with the coding of the facial action units associated with pain action based approach to describe the face. Expressions of painvaried in different faces. One of the factors that can create a variety of expression is style of the individual. These expression recognition requires a system that is person independent. In this system, interpersonal variations increase distance between training and test sample distribution, resulting in the loss of generalization of learning. In this paper, in order to reduce the distance between the two distributions, style transfer vector and mapping are proposed to improve pain and its intensity.Results of methods are investigated on the spontaneous UNBC-McMaster database. The results show that style transfer methodsimprove the recognition performance with a sufficient and small amount of adaptation data.