An evaluation framework and database for mocap-based gait recognition methods

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 comprises 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 class separability coefficients of feature space and four classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom probe on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.