Abstract
Biometric gait authentication using Personal Mobile Device (PMD) based accelerometer sensors offers a user-friendly, unobtrusive, and periodic way of authenticating individuals on PMD. In this paper, we present a technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique. We also present two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs); a) pre-computed data matrix, b) pre-computed kernel matrix. In the first approach, we used Dynamic Time Warping (DTW) distance to compute data matrices, and in the later DTW is used for constructing an elastic similarity measure based kernel function called Gaussian Dynamic Time Warp (GDTW) kernel. Both approaches utilize the DTW similarity measure and can be used for classifying equal length gait cycles, as well as different length gait cycles. To evaluate our approaches we used normal walk biometric gait data of 51 participants. This gait data is collected by attaching a PMD to the belt around the waist, on the right-hand side of the hip. Results show that these new approaches need to be studied more, and potentially lead us to design more robust and reliable gait authentication systems using PMD based accelerometer sensor.
Original language | English |
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Title of host publication | Proceedings - 11th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2013 |
Publisher | ACM Press |
Pages | 293-300 |
Number of pages | 8 |
ISBN (Print) | 978-1-4503-2106-8 |
DOIs | |
Publication status | Published - 2013 |
Event | 11th International Conference on Advances in Mobile Computing and Multimedia (MoMM2013) - Vienna, Austria Duration: 2 Dec 2013 → 4 Dec 2013 http://www.iiwas.org/conferences/momm2013/ |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 11th International Conference on Advances in Mobile Computing and Multimedia (MoMM2013) |
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Country/Territory | Austria |
City | Vienna |
Period | 02.12.2013 → 04.12.2013 |
Internet address |
Keywords
- Authentication
- accelerometer
- biometrics
- gait recognition
- machine learning