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

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%.