Improvement of a face recognition method for high jumper with a single sample based on Lucas-Kanade algorithm Online publication date: Thu, 29-Apr-2021
by Guoxing Shi
International Journal of Biometrics (IJBM), Vol. 13, No. 2/3, 2021
Abstract: In order to improve the identification accuracy of a dynamic single sample, a face recognition method based on Lucas-Kanade algorithm is proposed. The weighted Lucas-Kanade (LK) algorithm is used to obtain the single-sample affine transformation parameters of the high jumper's side face block and the corresponding front face block, and the optimal parameters of face pose correction are found through the maximum Gabor similarity, the method of face recognition for high jumper with a single sample is completed. Simulation results show that both the front face recognition rate and side-face recognition rate of the proposed method can reach more than 95% and the face recognition recall rate of the proposed method ranges from 90% to 100%. Compared with the traditional method, the recall rate has been significantly improved. In addition, when there are 440 face images, the recognition time is 1,177 ms, which is shorter than the traditional method.
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