From Face to Gait: Weakly-Supervised Learning of Gender Information from
Walking Patterns
- URL: http://arxiv.org/abs/2111.00538v1
- Date: Sun, 31 Oct 2021 16:34:54 GMT
- Title: From Face to Gait: Weakly-Supervised Learning of Gender Information from
Walking Patterns
- Authors: Andy Catruna, Adrian Cosma, Ion Emilian Radoi
- Abstract summary: We propose a weakly-supervised method for learning gender information of people based on their manner of walking.
We make use of state-of-the art facial analysis models to automatically annotate front-view walking sequences and generalise to unseen angles by leveraging gait-based label propagation.
Our results show on par or higher performance with facial analysis models with an F1 score of 91% and the ability to successfully generalise to scenarios in which facial analysis is unfeasible due to subjects not facing the camera or having the face obstructed.
- Score: 1.6114012813668934
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Obtaining demographics information from video is valuable for a range of
real-world applications. While approaches that leverage facial features for
gender inference are very successful in restrained environments, they do not
work in most real-world scenarios when the subject is not facing the camera,
has the face obstructed or the face is not clear due to distance from the
camera or poor resolution. We propose a weakly-supervised method for learning
gender information of people based on their manner of walking. We make use of
state-of-the art facial analysis models to automatically annotate front-view
walking sequences and generalise to unseen angles by leveraging gait-based
label propagation. Our results show on par or higher performance with facial
analysis models with an F1 score of 91% and the ability to successfully
generalise to scenarios in which facial analysis is unfeasible due to subjects
not facing the camera or having the face obstructed.
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