# Gait Recognition from Motion Capture Data

arXiv: https://arxiv.org/abs/1708.07755 · HTML: https://mlsanity.com/paper/1708.07755/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Understanding Person Identification through Gait](https://mlsanity.com/paper/2203.04179/) (2022)
- [Interpretable Gait Recognition by Granger Causality](https://mlsanity.com/paper/2206.06714/) (2022)
- [Personalized Pose Forecasting](https://mlsanity.com/paper/2312.03528/) (2023)

## Abstract (arXiv)

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.