An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

TL;DR

This paper introduces a comprehensive evaluation framework and a new database for gait recognition using MoCap data, facilitating reproducible research and comparison of different methods.

Contribution

It provides detailed implementation, source codes, and evaluation tools for state-of-the-art and novel gait recognition features from MoCap data.

Findings

Framework enables consistent evaluation of gait recognition methods.

Database allows benchmarking with standardized MoCap gait data.

Source codes facilitate reproducibility and method comparison.

Abstract

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from motion capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.