| dc.contributor.author | Bours, Patrick | |
| dc.contributor.author | Shrestha, Raju | |
| dc.date.accessioned | 2011-10-26T13:09:04Z | |
| dc.date.available | 2011-10-26T13:09:04Z | |
| dc.date.issued | 2010 | |
| dc.identifier.citation | Bours, P. & Shrestha, R. (2010). Eigensteps: A giant leap for gait recognition. I: 2nd International Workshop on Security and Communication Networks (IWSCN), 2010, IEEE conference proceedings. | en_US |
| dc.identifier.isbn | 9781424469383 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11250/142536 | |
| dc.description | This is the post-print version (ie final draft post-refereeing). Publisher's version/PDF is applicable on: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5497991.
Access to the published version may require journal subscription. | en_US |
| dc.description.abstract | In this paper we will show that using Principle Component Analysis (PCA) on accelerometer based gait data will give a large improvement on the performance. On a dataset of 720 gait samples (60 volunteers and 12 gait samples per volunteer) we achieved an EER of 1.6% while the best result so far, using the Average Cycle Method (ACM), gave a result of nearly 6%. This tremendous increase makes gait recognition a viable method in commercial applications in the near future. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.source | 2nd International Workshop on Security and Communication Networks (IWSCN), 2010 | en_US |
| dc.title | Eigensteps: A giant leap for gait recognition | en_US |
| dc.type | Chapter | en_US |
| dc.type | Peer reviewed | en_US |
| dc.subject.nsi | VDP::Mathematics and natural science: 400::Information and communication science: 420::Security and vulnerability: 424 | en_US |
| dc.source.pagenumber | 6 s. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.1109/IWSCN.2010.5497991 | |
