Micro-Expression Recognition Enhanced by Macro-Expression from Spatial-Temporal Domain

Authors: Bin Xia, Shangfei Wang

IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on three benchmark databases demonstrate the superiority of the proposed method.
Researcher Affiliation Academia Bin Xia1 and Shangfei Wang1,2 1Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China 2Anhui Robot Technology Standard Innovation Base xiabin@mail.ustc.edu.cn, sfwang@ustc.edu.cn
Pseudocode No The paper does not contain any pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology.
Open Datasets Yes There are three databases commonly used for MER, i.e., CASME II [Yan et al., 2014], SMIC [Li et al., 2013] and SAMM [Davison et al., 2016]. Two popular lab-collected databases, i.e., CK+[Lucey et al., 2010] and MMI[Pantic et al., 2005] are adopted as macro-expression.
Dataset Splits Yes Leave-one-subject-out (LOSO) cross-validation is used in all experiments.
Hardware Specification No The paper does not provide specific details about the hardware used for running the experiments.
Software Dependencies No The paper mentions using ResNet18 and Adam optimizer but does not specify version numbers for any software dependencies or libraries.
Experiment Setup Yes We set the sampling interval as s = 2k(k = 0, 1, 2, , sc 1). We use Eq.8 to update the parameter of Mi Net with the Adam optimizer, and set λ1 = λ4 = 0.1, λ2 = λ3 = 0.2.