Fast Hand Feature Extraction Based on Connected Component Labeling, Distance Transform and Hough Transform

Abstract

In hand gesture recognition or hand tracking systems relied on hand modeling methods, it is usually required to extract from a hand image some hand features. This paper presents a new robust method based on connected component labeling (CCL), distance transform (DT) and Hough transform (HT) to fast and precisely extract the center of the hand, the directions and the fingertip positions of all outstretched fingers on a skin color detection image. First, the method uses a simple but reliable technique that is performed on both the connected component labeling image and the distance transform image to extract the center of the hand and a set of features pixels, which are called distance-based feature pixels. Then, the Hough transform is calculated on these feature pixels to detect all outstretched fingers as lines. From the line detection result, the finger directions and the fingertip positions are determined easily and precisely. This method can be carried out fast and accurately, even when the skin color detection image includes hand, faces and some noise. Moreover, the number of distance-based feature pixels is usually not so high; therefore, the line detection process based on the Hough transform can be performed very fast. That can satisfy the demands of a real-time human-robot interaction system based on hand gestures or hand tracking.