Publication details

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



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

Faculty of Informatics

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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