LEARNet Dynamic Imaging Network for Micro Expression Recognition

TL;DR

This paper introduces LEARNet, a novel dynamic imaging network designed to enhance micro-expression recognition by capturing subtle facial muscle movements in videos, achieving significant improvements over existing methods.

Contribution

The paper proposes LEARNet, a hybrid network with accretion layers and cross-decoupled relationships, to effectively preserve and analyze micro-level facial features in micro-expression recognition.

Findings

LEARNet outperforms ResNet by up to 4.03% on benchmark datasets.

The model effectively preserves high- and micro-level facial features.

Significant improvements demonstrate LEARNet's effectiveness in micro-expression recognition.

Abstract

Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the dynamic representation of micro-expressions to preserve facial movement information of a video in a single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region. The LEARNet refines the salient expression features in accretive manner by incorporating accretion layers (AL) in the network. The response of the AL holds the hybrid feature maps generated by prior laterally connected convolution layers. Moreover, LEARNet architecture incorporates the cross decoupled relationship between convolution layers which helps in preserving the tiny but influential facial muscle change information. The visual responses of the proposed LEARNet depict the effectiveness of the system by preserving both high- and micro-level edge features of facial expression. The effectiveness of the proposed LEARNet is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC. The experimental results after investigation show a significant improvement of 4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II, CAS(ME)^2 and SMIC datasets respectively.