@article{MAKHILLJEAS2019141317997,
    title = {Person Identification Based on Euclidean Distance Transform},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {13},
    pages = {4312-4316},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.4312.4316},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.4312.4316},
    author = {Huu-Hung,Viet-Ha,Duc-Hoang and},
    keywords = {Person identification,distance transform,support vector machine,silhouette,gait,signature},
    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.}
    }