Eigensteps: A giant leap for gait recognition
Chapter, Peer reviewed
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Original versionBours, 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. http://dx.doi.org/10.1109/IWSCN.2010.5497991
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.
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