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FEDA: Fine-grained emotion difference analysis for facial expression recognition by Hanwei Liu; Huiling Cai; Qincheng Lin; Xiwen Zhang; Xuefeng Li; Hui Xiao is a Medicine article available to read on EtoBox.

What is FEDA: Fine-grained emotion difference analysis for facial expression recognition about?

Facial expression recognition (FER) has an important role in intelligent human–computer interaction. The complexity and confusion of target emotions and the subjectivity of observers make the definition of emotion categories controversial, and low accuracy has become a bottleneck in facial expression recognition analysis. To establish an emotion representation model with efficient facial expression recognition, this study presents FEDA, a fine-grained emotion difference analysis based on correlation, and explores the issue of emotion categories with appropriate intraclass correlation and interclass differences. First, a clustering algorithm is employed to obtain variable fine-grained emotion representations. Second, the correlation is objectively analysed through recognition and facial action units. Last, an emotion representation model that supports high-efficiency FER is obtained. Through a test with the FERPlus public dataset, the recognition accuracy rate reached 91.5% for the first time, verifying the rationality of our emotion representation model. Our experimental results can also support the effective establishment of emotion representation models based on facial expression

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Author

Hanwei Liu; Huiling Cai; Qincheng Lin; Xiwen Zhang; Xuefeng Li; Hui Xiao

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)