FUSION OF COLOR AND MODIFIED WEBER’S LAW DESCRIPTOR BASED HUMAN SKIN DETECTION
C. Prema and D. Manimegalai
DOI : 10.3844/jcssp.2014.1680.1691
Journal of Computer Science
Volume 10, Issue 9
Skin detection in images or videos plays a vital role in a wide range of image processing applications like face detection, face tracking, gesture analysis, human-computer interaction etc. Due to its high processing speed and invariant against rotation, it has received significant interest in pattern recognition and computer vision. The robustness of skin detection using color information depends on real world conditions such as background, noise, change of intensity and lightening effects. This situation can be improved by using texture as a descriptor to extract skin pixels in images. This study proposes color based skin detection algorithm (fusion of Cheddad’s approach with Cr of YCbCr color space) with a texture based skin location algorithm called Modified Weber’s Law Descriptions (MWLD) to evaluate region features. MWLD is based on the fact that the Human Visual System (HVS) is more sensitive in lumens contrast than absolute luminance values. In MWLD, the differential excitation and gradient orientation of the current pixel are considered to extract the texture features. For differential excitation, Just Noticeable Distortion (JND) is also used. Due to the absence of red component in Cheddad’s new color space for skin detection, the detection rate is not good for real time applications. To improve this situation, the Cr component of YCbCr color space is included to obtain the color features. According to experimental results, the proposed method exhibits satisfactory performance in terms of True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy with wide variations in size, color and orientation. Different window sizes for differential excitation such as 3×3, 5×5 and 7×7 are also taken to check the performance.
© 2014 C. Prema and D. Manimegalai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.