Real Time Vision Based Method for Finger Counting Through Shape Analysis with Convex Hull and PCA Techniques

Abstract

This paper presents a real time vision-based finger counting method combining convex-hull detection and PCA techniques. The method starts by segmenting an input image to detect the area corresponding to the observed hand. For that purpose, a skin color detection method is used to differentiate the foreground containing the hand from the image background. Lighting variation can affect the accuracy of the segmentation which has an impact on the functioning of the proposed method. In order to deal with the lighting variation problem, HLS color space is used to represent the colors. Hand contour is then calculated and fingertips are detected through the detection of the convex-hull and convexity defects of the hand shape. The use of convex-hull algorithm is simple and gives accurate results when more than one finger is observed in the input image but the accuracy decreases when it comes to deal with the case where only one finger is observed. To overcome this problem, principal component analysis technique (PCA) is used to analyze the hand shape and detect the case where only one finger is observed with better accuracy. The proposed method could be utilized in Human Computer Interaction System (HCI) where the machine reacts to each detected number. Both real and synthetic images are used to test and demonstrate the potential of our method.