Scholarlink

<p>Facial Expression Recognition (FER) has become increasingly relevant in engineering applications spanning security, healthcare, human-robot interaction, and intelligent systems. Accurate recognition of emotional states through facial cues is particularly important for enabling autonomous systems to interpret human intent in dynamic environments. This study presents a survey of recent advances in FER, with a focus on machine learning (ML) and deep learning (DL) methodologies developed between 2019 and 2024. Special attention is given to Facial Action Unit Detection (FAUD) as an intermediate and physiologically grounded representation for expression recognition. The analyzed literature is organized according to the final scope of the proposed models, distinguishing FAUD-oriented, FER-oriented, AU-assisted FER, and FAUD-to-FER approaches. Through this structured comparison, the survey identifies methodological trends, reported performance patterns, and the conditions under which FAUD-based intermediate modeling appears particularly advantageous for reliable FER.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine and deep learning in facial expression recognition: a survey based on facial action units

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月21日

查看全文
  • Mauricio Priego,
  • Itzel Aranguren,
  • Diego Oliva,
  • Arturo Valdivia-G,
  • Mario A. Navarro,
  • Marco Pérez-Cisneros

摘要

Facial Expression Recognition (FER) has become increasingly relevant in engineering applications spanning security, healthcare, human-robot interaction, and intelligent systems. Accurate recognition of emotional states through facial cues is particularly important for enabling autonomous systems to interpret human intent in dynamic environments. This study presents a survey of recent advances in FER, with a focus on machine learning (ML) and deep learning (DL) methodologies developed between 2019 and 2024. Special attention is given to Facial Action Unit Detection (FAUD) as an intermediate and physiologically grounded representation for expression recognition. The analyzed literature is organized according to the final scope of the proposed models, distinguishing FAUD-oriented, FER-oriented, AU-assisted FER, and FAUD-to-FER approaches. Through this structured comparison, the survey identifies methodological trends, reported performance patterns, and the conditions under which FAUD-based intermediate modeling appears particularly advantageous for reliable FER.