Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.

Human classification using gait features

GIANARIA, ELENA;GRANGETTO, Marco;LUCENTEFORTE, Maurizio;BALOSSINO, Nello
2014

Abstract

Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.
International Workshop on Biometrics, BIOMET 2014
Sofia; Bulgaria
23-24/06/2014
1st International Workshop on Biometrics, BIOMET 2014
Springer Verlag
8897
16
27
9783319133850
Elena Gianaria; Marco Grangetto; Maurizio Lucenteforte; Nello Balossino
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/153211
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