Abstract
With the development of computer technology, people have a growing demand for accurate facial expression recognition. This research refers to fuzzy theory, uses fuzzy rough set tools to build a fuzzy rough loss function, and designs the FR-CNN algorithm for fuzzy facial expression recognition. In order to verify the application effect of the designed algorithm, facial expression recognition experiments were carried out using BU-3DFE and Jaffe datasets. The experimental results show that the distance index data of the FR-CNN (AA kNN) and FR-CNN (Softmax) algorithms constructed based on the algorithm designed in this study is significantly lower than that of the comparison scheme on BU-3DFE datasets, and the similarity index is significantly higher than that of the comparison scheme. The Cosine similarity values of FR-CNN (Softmax) and FR-CNN (AA kNN) algorithms are 0.988 and 0.986, respectively. However, the FR-CNN algorithm is slightly slower than the comparison scheme in processing facial expression image data. It can be seen that the algorithm designed in this study has a certain application prospect in the task of fuzzy facial expression recognition.
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Liu, J. An improved cnn algorithm with hybrid fuzzy ideas for intelligent decision classification of human face expressions. Soft Comput 27, 5195–5204 (2023). https://doi.org/10.1007/s00500-023-07840-7
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DOI: https://doi.org/10.1007/s00500-023-07840-7