# From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns

arXiv: https://arxiv.org/abs/2111.00538 · HTML: https://mlsanity.com/paper/2111.00538/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Probabilistic End-to-end Noise Correction for Learning with Noisy Labels](https://mlsanity.com/paper/1903.07788/) (2019)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [Deep Metric Transfer for Label Propagation with Limited Annotated Data](https://mlsanity.com/paper/1812.08781/) (2018)
- [Age Group and Gender Estimation in the Wild with Deep RoR Architecture](https://mlsanity.com/paper/1710.02985/) (2017)
- [Emotion Recognition From Gait Analyses: Current Research and Future Directions](https://mlsanity.com/paper/2003.11461/) (2020)
- [WildGait: Learning Gait Representations from Raw Surveillance Streams](https://mlsanity.com/paper/2105.05528/) (2021)
- [A Framework using Contrastive Learning for Classification with Noisy Labels](https://mlsanity.com/paper/2104.09563/) (2021)
- [Ordinal Distribution Regression for Gait-based Age Estimation](https://mlsanity.com/paper/1905.11005/) (2019)

## Built on by (most cited first, 6 of 6)

- [GaitPT: Skeletons Are All You Need For Gait Recognition](https://mlsanity.com/paper/2308.10623/) (2023)
- [PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait](https://mlsanity.com/paper/2308.10631/) (2023)
- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild](https://mlsanity.com/paper/2402.08316/) (2024)
- [Learning to Simplify Spatial-Temporal Graphs in Gait Analysis](https://mlsanity.com/paper/2310.03396/) (2023)
- [Adaptive Multimodal Person Recognition: A Robust Framework for Handling Missing Modalities](https://mlsanity.com/paper/2512.14961/) (2025)

## Abstract (arXiv)

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.