Gesture recognition

In "Learning OpenCV" a method using histograms is suggested for basic gesture recognition. They suggested computing the histogram of the picture to detect the hand region, calculate the image gradient and then compute the histograms for the gesture.

This method, however, was not rotation invariant and we had interest in it being so. A similar method, in the sense of counting skin pixels, was used. It consisted in calculating the difference between the areas of the convex hull of the hand and its polygonal approximation. The result would be the convexity defects.

Steps:
1. Contour detection of the hand
2. Douglas-Peucker algorithm for polygonal approximation
3. Convex hull.
4. Calculate convex defects and consider the deepest points.

The problem with the convexity defects' deepest points, however, is that often there are points which are not of of our interest.

At first, the minimum bounding box was considered to see if it could be used to discard the unwanted points. The idea behind it was to fix a point of the box, join it with the estimated hand location (approximately at the centre of the hand) and compute the angle with the points. The problem was that it was not so easy to detect the orientation of the hand, and thus fix the point at the bounding box.

Another way was necessary and the distance of the points to the convex hull was considered. From observing the results we had so far, we noticed that the points at the valleys between fingers were at a further distance with respect to the unwanted points. The maximum distance to the convex hull was calculated and then the points which were at less than 0.6 of this maximum_distance were considered discardable, which generally worked fairly well. Important valleys were discarded sometimes, but they seemed to be more related to poor segmentation.