Benefits of Gaussian Convolution in Gait Recognition
Abstract
The first and still popular approach to gait recognition applies computer vision techniques
to appearance-based features of walking patterns. More recently, wearable sensors have become
attractive. The accelerometer is the most used one, being embedded in widespread mobile devices.
Related techniques do not suffer for problems like occlusion and point of view, but for intra-subject
variations caused by walking speed, ground type, shoes, etc. However, we can often recognize a
person from the walking pattern, and this stimulates to search for robust features, able to sufficiently
characterize this trait. This paper presents some preliminary experiments using the convolution with
Gaussian kernels to extract relevant gait elements. The experiments use the large ZJU-gaitacc public
dataset, and achieve improved results compared with previous works exploiting the same dataset.
- Citation
- BibTeX
Marsico, M. D. & Mecca, A.,
(2018).
Benefits of Gaussian Convolution in Gait Recognition.
In:
Brömme, A., Busch, C., Dantcheva, A., Rathgeb, C. & Uhl, A.
(Hrsg.),
BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group.
Bonn:
Köllen Druck+Verlag GmbH.
@inproceedings{mci/Marsico2018,
author = {Marsico, Maria De AND Mecca, Alessio},
title = {Benefits of Gaussian Convolution in Gait Recognition},
booktitle = {BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group},
year = {2018},
editor = {Brömme, Arslan AND Busch, Christoph AND Dantcheva, Antitza AND Rathgeb, Christian AND Uhl, Andreas},
publisher = {Köllen Druck+Verlag GmbH},
address = {Bonn}
}
author = {Marsico, Maria De AND Mecca, Alessio},
title = {Benefits of Gaussian Convolution in Gait Recognition},
booktitle = {BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group},
year = {2018},
editor = {Brömme, Arslan AND Busch, Christoph AND Dantcheva, Antitza AND Rathgeb, Christian AND Uhl, Andreas},
publisher = {Köllen Druck+Verlag GmbH},
address = {Bonn}
}
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More Info
ISBN: 978-3-88579-676-4
ISSN: 1617-5469
xmlui.MetaDataDisplay.field.date: 2018
Language:
(en)

Content Type: Text/Conference Paper