Robust Fingertip Detection in a Complex Environment

Abstract

Fingertip detection has a broad application in gesture recognition and finger tracking. It is also an important foundation of human–computer interaction systems. However, most algorithms are suitable for simple conditions with low accuracy because the hand is a nonrigid object, and its appearance model is complex. To address the challenging problem of accurately detecting fingertips in a complex environment, we propose a novel and robust fingertip detection algorithm in this paper. Unlike existing methods, our study requires no special device or mark, and users are free to move their hands. Via dense optical flow and a skin filter, we perform complete hand region segmentation in a complex environment. We find the maximum value of the local centroid distance outside the centroid circles and identify fingertips. Our algorithm performs favorably compared with common hand region segmentation and fingertip detection methods. Thorough experimentation proves that our proposed algorithm is effective and robust.