An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016) |
| MU Faculty or unit | |
| Citation | |
| web | |
| Doi | http://dx.doi.org/10.1007/978-3-319-56414-2_3 |
| Field | Informatics |
| Keywords | software evaluation framework; gait cycle database; human gait recognition |
| Attached files | |
| Description | 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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