Euclidean distance versus Manhattan distance for skin detection using the SFA database Online publication date: Thu, 09-Dec-2021
by Ouarda Soltani; Souad Benabdelkader
International Journal of Biometrics (IJBM), Vol. 14, No. 1, 2022
Abstract: Skin detection is very challenging because of the differences in illumination, cameras characteristics, the range of skin colours due to different ethnicities and many other variations. New effective and accurate methodologies are developed for skin colour detection to easily identify human's skin colour threw databases which are specifically designed to assist research in the area of face recognition. One of these is the recently built SFA database that showed high accuracy for segmentation of face images. The approach described in this paper exploits skin and non-skin samples provided by SFA for skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Most importantly, the scheme proposed tries to segment facial colour images inside or outside SFA by means of skin samples belonging to SFA. Simulation results in both SFA and UTD colour face databases indicate that detection rates higher than 95% can be achieved with either measure.
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