Micro-expressions are quick facial motions, appearing in high stake and stressful situations typically when a subject tries to hide his or her emotions. Two attributes are present - fast duration and low intensity. A simple detection method is proposed, which determines instants of micro-expressions in a video. The method is based on analyzing image intensity differences over a registered face sequence. The specific pattern is detected by an SVM classifier. The results are evaluated on standard microexpression datasets SMIC-E and CASMEII. The proposed method outperformed competing methods in detection accuracy. Further, we collected a new real micro-expression dataset of mostly poker game videos downloaded from YouTube. We achieved average cross-validation AUC 0.88 for the SMIC, and 0.81 on the new challenging "in the Wild" database.
Download:| Sample from the dataset (sub01-1, AU: L12, contempt). See all videos |