Despite advances in hand detection and hand tracking, robust hand segmentation remains a challenging task in many
gesture recognition systems. Problems can be caused by
a variety of factors, such as changing illumination and
background clutter. We compare the most commonly used
visual cues for hand segmentation, namely skin colour
and background subtraction, applied both separately and
combined. All three approaches are evaluated on video-data recorded with different backgrounds and under varying lighting conditions using a standard evaluation scheme
based on overlapping masks. Additionally, we introduce a
new evaluation scheme based on global histograms of oriented gradient
