# Robust Analytics for Video-Based Gait Biometrics

arXiv: https://arxiv.org/abs/2111.06670 · HTML: https://mlsanity.com/paper/2111.06670/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 256 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Acoustic Gait-based Person Identification using Hidden Markov Models](https://mlsanity.com/paper/1406.2895/) (2014)

## Built on by (most cited first, 1 of 1)

- [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2309.10144/) (2023)

## Abstract (arXiv)

Gait analysis is the study of the systematic methods that assess and quantify animal locomotion. Gait finds a unique importance among the many state-of-the-art biometric systems since it does not require the subject's cooperation to the extent required by other modalities. Hence by nature, it is an unobtrusive biometric. This thesis discusses both hard and soft biometric characteristics of gait. It shows how to identify gender based on gait alone through the Posed-Based Voting scheme. It then describes improving gait recognition accuracy using Genetic Template Segmentation. Members of a wide population can be authenticated using Multiperson Signature Mapping. Finally, the mapping can be improved in a smaller population using Bayesian Thresholding. All methods proposed in this thesis have outperformed their existing state of the art with adequate experimentation and results.