Micro-expression recognition based on motion detection method

Micro-expressions are emotional representations that occur spontaneously and cannot be controlled by humans. The micro-expression movements are temporary with fast duration and have subtle movements with little intensity. This is difficult to detect with the human eye. Previous studies have shown that micro-expression movements occur in several areas of the face. This study aims to determine the subtle movements in several areas of the face using the motion detection method. We compared the performance of two motion detection methods: the optical flow method and the Block Matching Algorithm (BMA) method. The optical flow method uses the Kanade-Lucas Tomasi (KLT) method and the BMA method uses the Phase Only Correlation (POC) algorithm. Observations were carried out based on region, where the face area was divided into several observation areas: eyebrows, eyes and mouth. Both methods perform motion detection between frames. The KLT method tracks the movement of the observation points on the frame movement. Meanwhile, the POC method matches the blocks between frames. If the two blocks are the same, no motion vector is generated. However, if the two blocks are different, it is assumed that there is a translational motion and a motion vector is generated. Experiments were conducted using a dataset from CASME II with emotional classes of disgust, happiness, surprise, and sadness. The classification accuracy of the POC method is 94% higher than the KLT method of 84.8% which uses the SVM classification.