While preprocessing techniques remove morphological facial differences, the proposed variant of AdaBoost deals with imbalanced characteristics of available subtle expression databases. Paul Ekman is the co-discoverer of micro expressions, the originator of training tools such as Micro Facial Expressions Training Tool (METT), Subtle Facial. After training, students in the high quartile showed significant improvement in the recognition of facial micro-expressions (P 0.014). No difference in pre-assessment scores was found between the lowest and highest quartile groups (P 0.797). This paper aims to solve the last two problems by first employing preprocessing steps: facial registration, cropping and interpolation and proposes a person-specific AdaBoost classifier with Selective Transfer Machine framework. Students were also invited to provide their views on the training. Meanwhile, subtle/micro-expressions are rarely analyzed, due to three main difficulties: inter-class similarity (hardly discriminate facial expressions of two subtle emotional states from a person), intra-class dissimilarity (different facial morphology and behaviors of two subjects in one subtle emotion state), and imbalanced sample distribution for each class and subject. commonly referred to as the Micro-expression Training Tool (Mett). Facial expression analysis has been well studied in recent years however, these mainly focus on domains of posed or clear facial expressions. The results showed that the new microexpression training developed for adolescents.

Subtle expression training tool