Static Hand Gestures: Fingertips Detection Based on Segmented Images

Fingertips detection is important for recognition of static gesture in sign language. This paper presents a new method that is based on YCbCr colour space and skeletonization for fingertips detection towards gesture segmentation and recognition. The method begins with the conversion of an image in RGB colour space into YCbCr colour space. Then, the chrominance Cb and Cr are extracted from the YCbCr colour space. A thresholding technique, which is based on a pre-defined range for Cb and Cr components representing the skin colour value is used to extract the hand from the background and to achieve the binary image. A morphological processing is performed on the obtained binary image to remove noise and unwanted image pixels. The candidate fingertips position is then calculated based on skeletonization algorithm and tracing process. The centroid is subsequently found, after which the Euclidean distances between the pixels’ coordinates that belong to the candidate fingertips and the centroid are calculated towards validating their representation of the actual fingertips. Based on the proposed method, the fingertips of twenty-six American Sign Language (ASL) alphabets hand sign samples were detected successfully and the gesture was correctly recognized with an accuracy of 96.3% during the conducted experiments.