Title: Recognition algorithm of athletes' partially occluded face based on a deep learning algorithm

Authors: Wenjuan Li; Kevin Millsap

Addresses: Department of Physical Education, Wuxi Taihu University, Wuxi, 214000, China ' College of Engineering, University of Iowa, Iowa 52242, USA

Abstract: Because there are a lot of noise data in the partially occluded face image, the existing recognition methods have the problems of low recall rate and long time consumption. In this paper, a new recognition algorithm based on a deep learning algorithm is proposed. This method uses boosting algorithm to locate face information, based on which the face image is greyed and denoised. The local binary pattern is used to extract face features, and the convolution neural network in deep learning algorithm is used to realise face feature recognition. The experimental results show that compared with the traditional face feature recognition algorithm, the proposed method has significantly improved recognition accuracy and recall rate, and the feature recognition time is shorter, which proves that the proposed algorithm has stronger application performance.

Keywords: deep learning algorithm; convolution neural network; athlete; local occlusion; face feature; recognition algorithm.

DOI: 10.1504/IJBM.2021.114647

International Journal of Biometrics, 2021 Vol.13 No.2/3, pp.305 - 321

Received: 24 Mar 2020
Accepted: 19 May 2020
Published online: 29 Apr 2021 *

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