Micro Expression Spotting through Appearance Based Descriptor and Distance Analysis

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

CFD: a collaborative feature difference method for spontaneous micro-expression spotting

Micro-expression (ME) is a special type of human expression which can reveal the real emotion that people want to conceal. Spontaneous ME (SME) spotting is to identify the subsequences containing SMEs from a long facial video. The study of SME spotting has a significant importance, but is also very challenging due to the fact that in real-world scenarios, SMEs may occur along with normal facial expressions and other prominent motions such as head movements. In this paper, we improve a state-of-the-art SME spotting method called feature difference analysis (FD) in the following two aspects. First, FD relies on a partitioning of facial area into uniform regions of interest (ROIs) and computing features of a selected sequence. We propose a novel evaluation method by utilizing the Fisher linear discriminant to assign a weight for each ROI, leading to more semantically meaningful ROIs. Second, FD only considers two features (LBP and HOOF) independently. We introduce a state-of-the-art MDMO feature into FD and propose a simple yet efficient collaborative strategy to work with two complementary features, i.e., LBP characterizing texture information and MDMO characterizing motion information. We call our improved FD method collaborative feature difference (CFD). Experimental results on two well-established SME datasets SMIC-E and CASME II show that CFD significantly improves the performance of the original FD