Title: A method for classifying and recognising the emotional states of dancers based on the spatiotemporal features of facial expressions

Authors: Yaotian Li; Zhaoping Wang

Addresses: General Education Center, Changsha Social Work College, Changsha, 410004, Hunan, China ' School of Software, Changsha Social Work College, Changsha, 410004, Hunan, China

Abstract: To address the issues of low recall and poor accuracy in the classification and recognition of dance performers' emotional states based on spatiotemporal features of facial expressions, a dance performer emotional state classification and recognition method based on spatiotemporal features of facial expressions is proposed. Firstly, face detection is performed using an integral graph, and pre-processing is carried out using affine transformation and histogram equalisation. Secondly, combining the LBP and LPQ algorithms to extract spatiotemporal has a features of facial expressions. Next, principal component analysis is applied for feature selection and dimensionality reduction to reduce noise and redundant information. Finally, support vector machine (SVM) is used for emotional state classification, achieving automatic recognition and multi class classification. Through experiments, it has been proven that the accuracy and recall rate of the emotional state recognition method proposed in this paper are high, with a recall rate consistently above 95%.

Keywords: spatiotemporal features; principal component analysis; PCA; support vector machine; SVM; emotional state; classification recognition.

DOI: 10.1504/IJBM.2026.151082

International Journal of Biometrics, 2026 Vol.18 No.1/2/3, pp.1 - 16

Received: 07 Nov 2024
Accepted: 28 Dec 2024
Published online: 13 Jan 2026 *

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