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<p>Facial Expression Analysis and Recognition (FEAR) are evolutionary concepts in the field of computer vision and artificial intelligence to develop human–computer interaction. Over the last decade, technology is leading human lives through online in various fields like education, medical, entertainment, shopping, etc. FEAR is a challenging task for the researchers while deploying the system in real-time applications as human expressions are not general in all situations and with all content. In experimentation of expression analysis and recognition, a feature extraction, database, and similarity measure play a key role to justify whether the system achieves the required target or not. This paper represents the various benchmark approaches for feature extraction, expression classification, and facial expression datasets like JAFFE, RaFD, CK + , MMI, FER2013 etc., and their features, representations, and challenges given in the investigations according to the researchers requirement so far. Retrospectively, discusses the suggestions and recommendations of the forthcoming mechanisms to reduce the semantic gap to analyze the expressions in general.</p>

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Techniques, promising directions, challenges, datasets, and representations of facial expression analysis

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • V. Uma Maheswari,
  • Rajanikanth Aluvalu,
  • Swapna Mudrakola,
  • Premkumar Chithaluru,
  • Manoj Kumar

摘要

Facial Expression Analysis and Recognition (FEAR) are evolutionary concepts in the field of computer vision and artificial intelligence to develop human–computer interaction. Over the last decade, technology is leading human lives through online in various fields like education, medical, entertainment, shopping, etc. FEAR is a challenging task for the researchers while deploying the system in real-time applications as human expressions are not general in all situations and with all content. In experimentation of expression analysis and recognition, a feature extraction, database, and similarity measure play a key role to justify whether the system achieves the required target or not. This paper represents the various benchmark approaches for feature extraction, expression classification, and facial expression datasets like JAFFE, RaFD, CK + , MMI, FER2013 etc., and their features, representations, and challenges given in the investigations according to the researchers requirement so far. Retrospectively, discusses the suggestions and recommendations of the forthcoming mechanisms to reduce the semantic gap to analyze the expressions in general.