A Pipeline for Hand 2-D Keypoint Localization Using Unpaired Image to Image Translation
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Date
2021-07-02Author
Farahanipad, Farnaz
Rezaei, Mohammad
Dilhoff, Alex
Kamangar, Farhad
Athitos, Vassilis
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Show full item recordAbstract
Hand pose estimation is getting a lot of attention in many areas such
as Human-Computer Interaction and Sign Language Recognition.
A fundamental step to accurately estimate the hand pose involves
detecting and localizing fingertips in an image. Despite the progress
of 2-D hand pose estimation in recent studies, accurate and robust
detection and localization of fingertips still remains a challenging
task due to low resolution of a fingertip in images and varying
lightning condition.
Inspired by the progress of the Generative Adversarial Network
(GAN) and image-style transfer, we propose a two-stage pipeline to
accurately localize the fingertip position even in varying lighting
and severe self occlusion on depth images. The idea is to use a
Cycle-consistent Generative Adversarial Network (Cycle-GAN) to
apply unpaired image-to-image translation and generate a depth
image with colored predictions on the fingertips, wrist, and palm
given a real depth image. The model is trained in a semi-supervised
manner using a collection of images from source and target domains
that do not need to be related in anyway. Then, by applying color
segmentation techniques, we localize the center of each colored area
which results in finding the location of each fingertip along with
center of the wrist and the palm. The proposed method achieves
visually promising results on noisy depth images captured using the
Microsoft Kinect. Experiments on the challenging NYU hand dataset
have demonstrated that our approach not only generates plausible
samples, but also outperforms state-of-the-art approaches on 2-D
fingertip estimation by a significant margin even in the presence
of severe self-occlusion and varying lighting conditions. Moreover,
fingertips would be detected irrespective of user orientation using
this method.