Gesture Recognition Accuracy

The authors of this paper explore two different methods of hand detection.  The first method is skin detection based on RGB values from the color camera, and the second is based on depth information.

The skin based detector works by trying to identify which area of the image is the user's hand by using, you guessed it, skin color.  A 'generic skin color histogram' is used to give each pixel of the image a probability of being skin.  The 'difference' (change in intensity between the current pixel and the pixels to it's side) is also computed, to help find clumps of pixels of the same color.  The sections with the most skin color are scored higher, and more likely to be the hand location.  This method doesn't always accurately find the hand, in the example in the paper it instead classified a person's face in the background as the hand.

The depth method finds abrupt changes in depth and uses that information to construct a binary image.  The five largest 'connected by depth' areas have their average depth calculated, and the section with the lowest mean depth is considered the person making the gesture.  The area is then filtered to determine the location of the hand.  The example of this method given in the paper did accurately find the hand.

The depth hand detector appeared to work the best in their experimental results as well, so perhaps that would be a good option for us to consider as well.


Source Link: http://vlm1.uta.edu/~athitsos/publications/doliotis_petra2011.pdf