Multi-view face pose recognition model construction based on a typical correlation analysis algorithm Online publication date: Thu, 29-Apr-2021
by Rongtao Liao; Yuzhe Zhang; Yixi Wang; Dangdang Dai
International Journal of Biometrics (IJBM), Vol. 13, No. 2/3, 2021
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook