Title: Emotion recognition algorithm of basketball players based on deep learning

Authors: Limin Zhou; Cong Zhang; Miao Wang

Addresses: Institute of Physical Education and Training, Harbin Sport University, Harbin 150001, China ' Institute of Physical Education and Training, Harbin Sport University, Harbin 150001, China ' Institute of Physical Education and Training, Harbin Sport University, Harbin 150001, China

Abstract: 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.

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

DOI: 10.1504/IJICT.2023.131223

International Journal of Information and Communication Technology, 2023 Vol.22 No.4, pp.377 - 390

Received: 04 Feb 2021
Accepted: 24 Apr 2021
Published online: 01 Jun 2023 *

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