Can Human Sex Be Learned Using Only 2D Body Keypoint Estimations?
- URL: http://arxiv.org/abs/2011.03104v2
- Date: Wed, 20 Apr 2022 07:05:43 GMT
- Title: Can Human Sex Be Learned Using Only 2D Body Keypoint Estimations?
- Authors: Kristijan Bartol and Tomislav Pribanic and David Bojanic and Tomislav
Petkovic
- Abstract summary: We present a fully automated classification system using only 2D keypoints.
A keypoint set consists of 15 joints and the keypoint estimations are obtained using an OpenPose 2D keypoint detector.
We learn a deep learning model to distinguish males and females using the keypoints as input and binary labels as output.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we analyze human male and female sex recognition problem and
present a fully automated classification system using only 2D keypoints. The
keypoints represent human joints. A keypoint set consists of 15 joints and the
keypoint estimations are obtained using an OpenPose 2D keypoint detector. We
learn a deep learning model to distinguish males and females using the
keypoints as input and binary labels as output. We use two public datasets in
the experimental section - 3DPeople and PETA. On PETA dataset, we report a 77%
accuracy. We provide model performance details on both PETA and 3DPeople. To
measure the effect of noisy 2D keypoint detections on the performance, we run
separate experiments on 3DPeople ground truth and noisy keypoint data. Finally,
we extract a set of factors that affect the classification accuracy and propose
future work. The advantage of the approach is that the input is small and the
architecture is simple, which enables us to run many experiments and keep the
real-time performance in inference. The source code, with the experiments and
data preparation scripts, are available on GitHub
(https://github.com/kristijanbartol/human-sex-classifier).
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