Title: A method of swimmer emotion recognition based on sequence annotation model

Authors: Yangjun Liu; Junying Yang

Addresses: School of Physical Education and Health, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, Sichuan, China; Faculty of Physical Culture, Gdansk University of Physical Education and Sport, Gdansk, 80-336, Poland ' Media Art College, Sichuan University Jinjiang College, Meishan, 620860, Sichuan, China; College of Chinese and Asean Arts, Chengdu University, Chengdu, 610106, Sichuan, China

Abstract: In order to improve the accuracy of swimmer emotion recognition and shorten the time spent on emotion recognition; this paper proposes a swimmer emotion recognition method based on sequence annotation model. Firstly, the original face image of a swimmer is collected, and the collected face image is integrally processed. First, the original face image is restored through linear equations to obtain a clear face image of the swimmer. Secondly, a face image conversion marker matrix is constructed to match the key points and singular values of the image to extract the features of the swimmer's face image. Then, a sequence annotation model is used to classify the expression features of swimmers. Finally, using fuzzy kernel discriminant analysis technology to complete the swimmer's emotional recognition. Experimental results show that this method can accurately recognise swimmers' facial expressions, with a recognition accuracy of up to 100%.

Keywords: sequence annotation model; emotional recognition; face image; singular value matching; swimmers.

DOI: 10.1504/IJRIS.2025.147453

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.3, pp.173 - 180

Received: 31 Mar 2023
Accepted: 15 May 2023
Published online: 16 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article