Paper Info
Title
Suppressing False Nagatives in Skin Segmentation
Abstract
Human skin segmentation in colored images is closely related to face detection and recognition systems as preliminary required step. False negative errors degrade segmentation accuracy and therefore considered as critical problem in image segmentation. A general innovative approach for human skin segmentation that substantially suppresses false negative errors has been developed. This approach employed multi-skin models using HSV color space. Four skin color clustering models were used, namely: standard-skin model, shadow-skin model, light-skin model, and redness-skin model. The color information was used to segment skin-like regions by transforming the 3-D color space to 2-D subspace. A rule-based classifier produces four skin-maps layers. Each layer reflects its skin model. Pixel-based segmentation and region-based segmentation approaches has been combined to enhance the accuracy. The inspiring results obtained show that the suppression of false negatives is substantial and leads to better detection and recognition.
Year
DOI
Venue
2010
10.1007/978-3-642-17569-5_15
Lecture Notes in Computer Science
Keywords
Field
DocType
Human skin segmentation,skin color modelling,face detection,HSV
Computer vision,HSL and HSV,Color space,Pattern recognition,Color histogram,Segmentation,Computer science,Image segmentation,Artificial intelligence,Pixel,Face detection,Cluster analysis
Conference
Volume
ISSN
Citations 
6485
0302-9743
1
PageRank 
References 
Authors
0.34
14
3
Name
Order
Citations
PageRank
Roziati Zainuddin1658.91
Sinan A. Naji2182.12
aljaafar roziati zainuddin sinan naji and jubair310.34