Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
This study presents a method of person identification working on a sequence of 2D silhouettes. Inspired by the state-of-the-art gait signature gait energy image, we integrate a non-linear operation into the stage of feature extraction in order to enhance the obtained gait signature. Unlike related works, our method does not require the step of gait cycle separation. The accuracy of our approach was demonstrated by experiments on the CASIA-A gait dataset and was comparable with many related studies.
How to cite this article:
Viet-Ha Ho, Duc-Hoang Vo, Van-Sy Ngo and Huu-Hung Huynh. Person Identification Based on Euclidean Distance Transform.
DOI: https://doi.org/10.36478/jeasci.2019.4312.4316
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.4312.4316