@article {6756,
	title = {Euclidean distance versus Manhattan distance for skin detection using the SFA database},
	journal = {International Journal of Biometrics},
	volume = {14},
	number = {1},
	year = {2022},
	pages = {46-60},
	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{\textquoteright}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.},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2022.119553},
	author = {Soltani , Ouarda and BENABDELKADER, SOUAD}
}
