A robust automatic micro-expression recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expression databases and built an improved one (CASME II), with higher temporal resolution (200 fps) and spatial resolution (about 2806340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressions were selected for the database with action units (AUs) and emotions labeled. For baseline evaluation, LBP-TOP and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validation method. The best performance is 63.41% for 5-class classification.
The performance of REF on recognising micro-expressions in 5-classes with LBP-TOP features extraction, achieved a best result of 63.41% accuracy, using leave-one-out cross-validation.
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation
PLoS ONE
PLoS ONE
Computer Science Medicine