FMeAR: FACS Driven Ensemble Model for Micro-Expression Action Unit Recognition

Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial expression, it is difficult to determine when spontaneous and low-intensity micro-expression occurs. The paper presents FMeAR which is a Facial action coding scheme (FACS) driven ensemble model for the micro-expression action unit recognition. Considering the simultaneous triggering of multiple facial AUs (action units), the problem is designed as a multiple-binary-class framework. First, each frame of the video is extracted and pre-processed thoroughly to eliminate any noise in the input. Then, features are extracted using an LBP descriptor from the pre-processed frames. To reduce the processing time, the size of the feature vector is reduced using PCA. Finally, reduced features are processed using a multiple ensemble model (comprising of Support Vector Machine, Linear Discriminant Analysis, and Decision Tree classifiers) to identify the micro-expressions action units. The presented model, FMeAR, outperforms several baseline frameworks and shows an accuracy of 95.3% for the CASME dataset, 95.9% for CASME-II, and 92.6% for MEVIEW. Intensive experiments demonstrated the effectiveness and generalization of FMeAR emphasizing its potential for various applications such as deception detection, depression detection, medical diagnosis, and many more.