Recognizing Micro-Expressions on Composite Databases with a Lightweight Approach

@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},
  }