Abstract
Facial Expression Recognition is a visual cue used for conveying emotions and intentions between human beings. The micro-expressions (MEs) are not visible to the human eye, making it challenging to capture the minute changes in the facial areas as the expressions change. As a result, automating the detection of ME is a challenging task. This work utilizes Delaunay Triangulation and Voronoi Diagram properties to segment Region of Interest (ROI) based on Action Unit indexes. The ROI-based feature extraction aided in improving the performance of the Micro-Expression Recognition (MER) system. The Cross-Database Evaluation (CDE) and Holdout Database Evaluation (HDE) are performed on three publicly available datasets CASMEII, SAMM, and SMIC (HS). The proposed approach resulted in an improved Unweighted Average Recall (UAR) and Unweighted F1 (UF1) scores by 6.09% and 4.36%, respectively. The results obtained with CDE and HDE demonstrate that the proposed model is robust compared to earlier studies.
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