Emotion recognition algorithm of basketball players based on deep learning

被引：0

作者：

Zhou L. ^{[1
]}

Zhang C. ^{[1
]}

Wang M. ^{[1
]}

机构：

[1] Institute of Physical Education and Training, Harbin Sport University, Harbin

来源：

International Journal of Information and Communication Technology | 2023年 / 22卷 / 04期

关键词：

deep learning; dimensionality reduction method; emotion recognition; facial expression feature extraction;

D O I：

10.1504/IJICT.2023.131223

中图分类号：

学科分类号：

摘要：

Aiming at the problems of traditional methods of emotion recognition accuracy, long recognition time and low recognition rate, a basketball player emotion recognition algorithm based on deep learning is proposed. Based on the Emotic dataset, a basketball remote mobilisation emotion recognition dataset is constructed to realise emotion classification. The LBP method is used to extract the facial expression features in the dataset, and the KDIsomap algorithm is used to perform nonlinear dimensionality reduction on the features according to the feature extraction results. According to the deep learning algorithm, the SVM classifier is combined with the KNN classification to form an SVM-KNN classifier to recognise the emotions of basketball players. Experimental results show that the shortest recognition time of the proposed algorithm is only 4.38 s, the highest recognition accuracy rate reaches 94.2%, and the recognition rate is high, indicating that the algorithm has a certain effectiveness. Copyright © 2023 Inderscience Enterprises Ltd.

引用

页码：377 / 390

页数：13

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