Gait Recognition from Motion Capture Data

TL;DR

This paper introduces a statistical machine learning method for gait recognition from motion capture data, outperforming previous geometric feature-based methods and emphasizing the generalizability and practical applicability of the approach.

Contribution

It proposes a modified Linear Discriminant Analysis with Maximum Margin Criterion for robust gait feature extraction directly from raw data, enhancing recognition accuracy and portability.

Findings

Outperforms 13 relevant geometric feature methods

Features are highly portable across subjects

Provides publicly available evaluation framework and database

Abstract

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.