Micro Expression Spotting through Appearance Based Descriptor and Distance Analysis

Abstract

Micro-Expressions (MEs) are a typical kind of expressions which are subtle and short lived in nature and reveal the hidden emotion of human beings. Due to processing an entire video, the MEs recognition constitutes huge computational burden and also consumes more time. Hence, MEs spotting is required which locates the exact frames at which the movement of ME persists. Spotting is regarded as a primary step for MEs recognition. This paper proposes a new method for ME spotting which comprises three stages; pre-processing, feature extraction and discrimination. Pre-processing aligns the facial region in every frame based on three landmark points derived from three landmark regions. To do alignment, an in-plane rotation matrix is used which rotates the non-aligned coordinates into aligned coordinates. For feature extraction, two texture based descriptors are deployed; they are Local Binary Pattern (LBP) and Local Mean Binary Pattern (LMBP). Finally at discrimination stage, Feature Difference Analysis is employed through Chi-Squared Distance (CSD) and the distance of each frame is compared with a threshold to spot there frames namely Onset, Apex and Offset. Simulation done over a Standard CASME dataset and performance is verified through Feature Difference and F1-Score. The obtained results prove that the proposed method is superior than the state-of-the-art methods.