Rotation-invariant hand posture classification with a convexity defect histogram
Ju-Hyeon Hong, Eung Sup Kim, Hyuk‐Jae Lee · 2012
Hand posture classification is popular in systems that require an effective human-machine interface. Previous classification algorithms suffer from inaccurate results when it is difficult to distinguish a hand from a wrist. To overcome this difficulty, this paper proposes a new algorithm for hand posture classification that uses a histogram of convex defects around the segment of a hand to be classified. As the characteristics of convex defects do not vary significantly depending on inclusion of a wrist, the proposed algorithm does not suffer substantially from a reduced classification accuracy. Furthermore, the proposed algorithm is also rotation-invariant. Experimental results show that the correct classification ratio is 97.06% on average.