A comparative study on movement feature in different directions for micro-expression recognition

TL;DR

This study compares movement features in different directions for micro-expression recognition, introducing HSDG and LBP-SDG features, and finds that certain directions are more discriminative, achieving state-of-the-art results.

Contribution

It proposes a new low-dimensional feature, HSDG, and combines it with LBP-TOP to analyze directional discriminability in micro-expression recognition.

Findings

HSDG in an optimal direction is highly discriminative.

LBP-SDG with EVM achieves state-of-the-art performance.

Certain movement directions are more effective for recognition.

Abstract

Micro-expression can reflect people's real emotions. Recognizing micro-expressions is difficult because they are small motions and have a short duration. As the research is deepening into micro-expression recognition, many effective features and methods have been proposed. To determine which direction of movement feature is easier for distinguishing micro-expressions, this paper selects 18 directions (including three types of horizontal, vertical and oblique movements) and proposes a new low-dimensional feature called the Histogram of Single Direction Gradient (HSDG) to study this topic. In this paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP with Single Direction Gradient (LBP-SDG) and analyze which direction of movement feature is more discriminative for micro-expression recognition. As with some existing work, Euler Video Magnification (EVM) is employed as a preprocessing step. The experiments on the CASME II and SMIC-HS databases summarize the effective and optimal directions and demonstrate that HSDG in an optimal direction is discriminative, and the corresponding LBP-SDG achieves state-of-the-art performance using EVM.