Precise Hand Finger Width Estimation via RGB-D Data

We propose a novel application of pose estimation to precisely measure the hand finger width via noisy RGB-D image. A framework is developed that estimates the finger width given data from TrueDepth camera as well as the target finger measure- ment location. Moreover, handPifPaf, a new bottom-up 2D hand pose estimator, is introduced and integrated with the width estimation pipeline. This network performs on a par with the state of the art hand pose estimators on public hand datasets. An extensive 2D annotated RGB hand dataset is built for the real-time application of handPifPaf on the width estimation pipeline. Finally, one unique large-scale hand RGB-D dataset is acquired for the finger width estimation pipeline validation. This set contains real hand data from various subjects, configurations and camera-object distances with exact ground truth finger width measurements at an especific target location.