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Eigensteps: A giant leap for gait recognition
2010
2010 2nd International Workshop on Security and Communication Networks (IWSCN)
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.
doi:10.1109/iwscn.2010.5497991
fatcat:7amb7upc6bazxmvoftib56fzyq