LAUSR

Recent progress in computer vision has pushed the limit of facial recognition from human identification to micro-expressions (MEs). However, the visual analysis of MEs is still a very challenging task because of the short occurrence and insignificant intensity of the underlying signals. To date, the accuracy of recognizing hidden emotions from frames using conventional methods is still far from reaching saturation. To address this, we have proposed a new ME recognition approach based on Mean Local Mapped Pattern (M-LMP) as a texture feature, which outperforms other state-of-the art features in terms of accuracy due to its capability of capturing small pixel transitions. Inspired by previous work, we applied M-LMP to the difference image computed from an onset frame and an apex frame, where the former represents the frame with neutral emotion and the latter consists of the frame with the largest ME intensity. The extracted local features were classified using support vector machine (SVM) and K nearest neighbourhood (KNN) classifiers. The validation of the proposed approach was performed on the CASME II and CAS(ME)2 datasets, and the results were compared with other similar state-of-the-art approaches. Comprehensive experiments were conducted using various parameters to show the robustness of our approach in the imbalanced and small dataset.