43 citations · 76 across the 6 of their papers we have counts for
6 papers
Interpretable Gait Recognition by Granger Causality
Michal Balazia, Katerina Hlavackova-Schindler, Petr Sojka +1
Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We pro…
Gait Recognition from Motion Capture Data
Michal Balazia, Petr Sojka
Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art wi…
You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
Michal Balazia, Petr Sojka
This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video…
An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
Michal Balazia, Petr Sojka
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 fro…
Walker-Independent Features for Gait Recognition from Motion Capture Data
Michal Balazia, Petr Sojka
MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new i…
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
Michal Balazia, Petr Sojka
In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers. To refrain from ad-hoc schem…