Title: Facial expression recognition of aerobics athletes based on CNN and HOG dual channel feature fusion

Authors: Shitao Wang; Jing Li

Addresses: College of Sports and Arts, Jilin Sports University, Changchun 130022, China ' College of Sports Human Science, Jilin Sports University, Changchun 130022, China

Abstract: The problem of low feature extraction accuracy and low recognition accuracy in facial expression recognition of aerobics athletes is presented. A recognition method for fusion CNN and HOG dual channel features is proposed. The basic principle of the HOG is analysed, and the facial expression image of aerobics athletes is processed by grey level with the help of local binary mode. The pixel gradient intensity value in each small image is obtained, and all the intensity values are fused. Lagrange formula is used to transform high-dimensional features. Support vector machine is used to classify facial expression images, and feature points are used as CNN, to process feature points according to network input. Regularisation regression is used to realise facial expression recognition of aerobics athletes. The experimental results show that the accuracy of feature extraction is 97% and the recognition accuracy is always higher than 90%.

Keywords: CNN; HOG; local binary mode; pixel gradient intensity value; facial expression.

DOI: 10.1504/IJICT.2023.129867

International Journal of Information and Communication Technology, 2023 Vol.22 No.3, pp.281 - 293

Received: 26 Jan 2021
Accepted: 26 Mar 2021
Published online: 03 Apr 2023 *

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