Title: Dynamic facial expression recognition of sprinters based on multi-scale detail enhancement

Authors: Xiang Cao; Pengfei Li

Addresses: Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, 451460, China ' Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, 451460, China

Abstract: In order to solve the problems of low average gradient and long recognition time in traditional facial expression recognition method, a multi-scale detail enhancement method for facial dynamic expression recognition of sprint athletes is proposed. A principal component analysis method was used to establish the facial expression feature subspace of sprinters, to project and reduce the dimension of the facial dynamic expression feature vector of sprinters, and to obtain the low frequency information and high frequency information of the facial image of sprinters by bilateral filtering. The multi-scale details of expression are enhanced by using side suppression network model and improving image S curve. The feature vector of facial dynamic expression is input into support vector machine to recognise the facial dynamic expression of sprinter. Experimental results show that the average value of annoying gradient is about 98 and the shortest time s. is about 1.9.

Keywords: multi-scale; image detail enhancement; Gabor wavelet transform; feature vector; expression recognition; support vector machine.

DOI: 10.1504/IJBM.2022.124675

International Journal of Biometrics, 2022 Vol.14 No.3/4, pp.336 - 351

Received: 16 Sep 2020
Accepted: 17 Nov 2020
Published online: 05 Aug 2022 *

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