  Hand pose estimation (HPE) is a task that predicts and describes the hand
poses from images or video frames. When HPE models estimate hand poses captured
in a laboratory or under controlled environments, they normally deliver good
performance. However, the real-world environment is complex, and various
uncertainties may happen, which could degrade the performance of HPE models.
For example, the hands could be occluded, the visibility of hands could be
reduced by imperfect exposure rate, and the contour of hands prone to be
blurred during fast hand movements. In this work, we adopt metamorphic testing
to evaluate the robustness of HPE models and provide suggestions on the choice
of HPE models for different applications. The robustness evaluation was
conducted on four state-of-the-art models, namely MediaPipe hands, OpenPose,
BodyHands, and NSRM hand. We found that on average more than 80\% of the hands
could not be identified by BodyHands, and at least 50\% of hands could not be
identified by MediaPipe hands when diagonal motion blur is introduced, while an
average of more than 50\% of strongly underexposed hands could not be correctly
estimated by NSRM hand. Similarly, applying occlusions on only four hand joints
will also largely degrade the performance of these models. The experimental
results show that occlusions, illumination variations, and motion blur are the
main obstacles to the performance of existing HPE models. These findings may
pave the way for researchers to improve the performance and robustness of hand
pose estimation models and their applications.
