See a list of publications below or visit the Photonics academic staff page and click on a particular member of staff to access their personal web page, which includes a list of their own publications.
@article{Chakraborty:2017:10.1016/j.patrec.2017.01.005,
author = {Chakraborty, BK and Bhuyan, MK and Kumar, S},
doi = {10.1016/j.patrec.2017.01.005},
journal = {Pattern Recognition Letters},
pages = {33--40},
title = {Combining image and global pixel distribution model for skin colour segmentation},
url = {http://dx.doi.org/10.1016/j.patrec.2017.01.005},
volume = {88},
year = {2017}
}
TY - JOUR
AB - Skin colour detection in unconstrained and natural environment is a critical research problem. Skin regions show slightly different chrominance properties for different ambient conditions. Accuracy of detection is mostly affected either due to background colour similarity with the skin and/or poor illumination condition. To address this specific issue, a novel skin detection method is proposed by utilizing the information of pixel distribution in an image for a particular colour space. In our method, a local skin distribution model (LSDM) is derived from the image pixel distribution model using pixels from the facial region as reference. Finally, a combined skin distribution model is obtained by fusing the LSDM with the global skin colour distribution model. Subsequently, a dynamic region growing (DRG) method is proposed to allow the skin regions to grow dynamically. Our proposed DRG minimizes the overall detection error. Experimental results show that proposed skin detection method can more accurately segment out the skin-coloured regions. We obtained total detection error of 12.87% for a standard database. This improvement is due to the fact that both local and global skin colour information are used for skin segmentation.
AU - Chakraborty,BK
AU - Bhuyan,MK
AU - Kumar,S
DO - 10.1016/j.patrec.2017.01.005
EP - 40
PY - 2017///
SN - 0167-8655
SP - 33
TI - Combining image and global pixel distribution model for skin colour segmentation
T2 - Pattern Recognition Letters
UR - http://dx.doi.org/10.1016/j.patrec.2017.01.005
VL - 88
ER -