Title: Multi-view face pose recognition model construction based on a typical correlation analysis algorithm

Authors: Rongtao Liao; Yuzhe Zhang; Yixi Wang; Dangdang Dai

Addresses: Information and Communication Branch of Hubei EPC, Wuhan, Hubei 430077, China ' School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China ' Information and Communication Branch of Hubei EPC, Wuhan, Hubei 430077, China ' Information and Communication Branch of Hubei EPC, Wuhan, Hubei 430077, China

Abstract: In order to overcome the problems of large recognition error and low recognition accuracy in the existing face pose recognition models, the paper proposes and constructs a new multi-view face pose recognition model based on typical correlation analysis algorithm. First, the AdaBoost algorithm is used to realise multi-pose face detection and positioning. Secondly, the face image is pre-processed, including image greying, image denoising, and face image geometric normalisation, and then the typical correlation analysis algorithm is used to extract face features. Finally, multi-view facial gesture recognition is realised through convolutional neural network. Experimental results show that, compared with the traditional recognition model, the recognition accuracy of the constructed model is greatly improved, and the average accuracy (mAP) is 96.334%, which proves that the recognition performance of the constructed model is better.

Keywords: canonical correlation analysis algorithm; convolution neural network; multi view face; gesture recognition.

DOI: 10.1504/IJBM.2021.114654

International Journal of Biometrics, 2021 Vol.13 No.2/3, pp.289 - 304

Received: 29 May 2020
Accepted: 25 Jul 2020
Published online: 29 Apr 2021 *

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