Hand gesture recognition based on convexity approach and background subtraction

This paper presents a method for hand gesture recognition using convexity defect and background subtraction. First, the background subtraction is used to eliminate the useless information. To find contour of segmented hand images we used images processing techniques. After that we calculate the convex hull and convexity defects for this contour. The feature extraction purposes to detect and extract features that can be used to determine the significance of a given hand gesture. The features must be able to characterize gesture only, and invariant under translation and rotation of hand gesture to ensure reliable recognition. We propose a method to extract a series of features based on convex defect detection, catching advantage of the close relationship of convex defect and fingertips. This method is mere, efficient and free from gesture direction and position. We have tested five hand gestures classes to show using one, two, three, four, and five fingers one by one.