Proceedings Article10.1109/IIHMSP.2010.84
Improved Cycle Detection for Accelerometer Based Gait Authentication
Mohammad Derawi,Patrick Bours,Kjetil Holien +2 more
- 15 Oct 2010
- pp 312-317
183
TL;DR: An improved biometric gait recognition approach with a stable cycle detection mechanism and comparison algorithm and can improve the performance, by using simple approaches is proposed.
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Abstract: Over the last years, there has been an increasing research interest in the application of accelerometry data for many kinds of automated gait analysis algorithms. The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose an improved biometric gait recognition approach with a stable cycle detection mechanism and comparison algorithm. Unlike previous work on wearable gait recognition, which was based from simple average cycling methods to more complicated methods, this paper reports new techniques for which can improve the performance, by using simple approaches. Preprocessing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 60 volunteers and 12 sessions each volunteer and resulted in an equal error rate (EER) of 5.7%.
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Citations
Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications.
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Inertial Sensor-Based Gait Recognition: A Review
TL;DR: Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait.
The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication
TL;DR: This paper presents the largest inertial sensor-based gait database in the world, which is made open to the research community, and its application to a statistically reliable performance evaluation for gait-based personal authentication.
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