The automated estimation of the psycho-emotional state of the human and their emotional reactions to the different influences using the video image analysis is an urgent task in different fields, such as: safeguarding in manufacturing, aviation, transportation, prevention of the crimes and terroristic threats, marketing researches etc. A promising direction is the facial micro-expression analysis. The facial micro-expressions are not under conscious control and reflect the objective emotional reaction. One of the key stages of the procedure of the automatic emotion estimation by the facial micro-expressions is the correct facial landmark detection. It is a complex task because of the presence of the different noise in the consecutive frames. Purpose – the investigation of the ways of increasing the performance of the facial micro-expression analysis pipeline by using preliminary video image processing procedures. It is shown that, as the preliminary stage of the micro-expression analysis pipeline, it is reasonable to perform the blurring of the original images to obtain the more stable results. The determined filtering parameters provide the MediaPipe framework a performance increase for the micro-expression analysis problems. It is shown that the video image blurring by the Gaussian filter with a size of 15×15 pixels allows to reduce the noise influence and to decrease the incorrect shifts of the facial landmarks from frame to frame induced by this noise. The proposed procedure of preliminary video image processing can be used for increasing the facial micro-expression recognition performance in emotion recognition systems based on the video sequence analysis.
Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms
