the process of separating the hand area from a complex background, known
as hand segmentation, is a prerequisite for any vision-based hand gesture recognition
system. In some applications, only a rough estimate of the hand area is needed, but in other
applications an exact segmentation of the hand, if possible, is needed. In this paper, three
methods for extracting the hand from the background in real-time were tested. These
methods use off-line learning of skin color; these methods are "Histogram Intersection",
"Color Histogram", and "Skin Color Modeling and Adaptation". The three methods were
tested using a video sequence of 100 frames using four different lighting conditions, with
400 frames in total. The different lighting conditions are fluorescent light only, a mixture of
fluorescent and daylight, daylight without the sun light, and daylight with the sun light.
These video streams were taken from the same person under the above different lighting
conditions. A comparative study between the three methods was performed. The results
showed that hand segmentation using "Skin Color Modeling and Adaptation" with
Fluorescent light produced the best results among the three methods. The objective of our
research is to accurately recognize the hand gestures, and then use it in many different
applications. The work in this paper is the first stage in our research. The contribution in
this paper is the comparative study between three different techniques that use skin-color
modeling under different lighting conditions.