Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach

• Author(s): Malik Jawarneh
• Paper ID: 1704350
• Page: 627-635
• Published Date: 28-04-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 10 April-2023

Abstract

Micro-expression recognition is an essential task in facial expression analysis that provides insight into human emotional states. However, traditional micro-expression recognition techniques require significant computational resources and time-consuming training processes, making them unsuitable for real-time and lightweight applications. To address this issue, this paper proposes a novel lightweight micro-expression recognition approach using composite databases. The proposed method leverages a combination of multiple public micro-expression databases to improve recognition performance while reducing computational costs. Our experimental results demonstrate the effectiveness of the proposed approach on the CASME II, CASME, SMIC, and SAMM micro-expression databases.

Keywords

Micro-expression recognition, Lightweight, Composite databases, CASME II, CASME, SMIC, SAMM.

Citations

IRE Journals:
Malik Jawarneh "Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach" Iconic Research And Engineering Journals Volume 6 Issue 10 2023 Page 627-635

IEEE:
Malik Jawarneh "Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach" Iconic Research And Engineering Journals, vol. 6, no. 10, Apr. 2023

APA:
Malik Jawarneh (2023). Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach. Iconic Research And Engineering Journals, 6(10).

MLA:
Malik Jawarneh "Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach" Iconic Research And Engineering Journals, vol. 6, no. 10, Apr. 2023.

BibTeX

@article{1704350,
author = {Malik Jawarneh},
title = {Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {10},
pages = {627-635},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704350.pdf},
abstract = {Micro-expression recognition is an essential task in facial expression analysis that provides insight into human emotional states. However, traditional micro-expression recognition techniques require significant computational resources and time-consuming training processes, making them unsuitable for real-time and lightweight applications. To address this issue, this paper proposes a novel lightweight micro-expression recognition approach using composite databases. The proposed method leverages a combination of multiple public micro-expression databases to improve recognition performance while reducing computational costs. Our experimental results demonstrate the effectiveness of the proposed approach on the CASME II, CASME, SMIC, and SAMM micro-expression databases.},
keywords = {Micro-expression recognition, Lightweight, Composite databases, CASME II, CASME, SMIC, SAMM.},
month = {April}
}