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arXiv · 1701.00995

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

Michal Balazia·Petr Sojka
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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.

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Michal Balazia, Petr Sojka. 2017-01-04. An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods. https://doi.org/10.1007/978-3-319-56414-2_3

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