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Accelerometer-Based Gait Recognition by Sparse Representation of Signature Points With Clusters.

Authors :
Zhang, Yuting
Pan, Gang
Jia, Kui
Lu, Minlong
Wang, Yueming
Wu, Zhaohui
Source :
IEEE Transactions on Cybernetics; Sep2015, Vol. 45 Issue 9, p1864-1875, 12p
Publication Year :
2015

Abstract

Gait, as a promising biometric for recognizing human identities, can be nonintrusively captured as a series of acceleration signals using wearable or portable smart devices. It can be used for access control. Most existing methods on accelerometer-based gait recognition require explicit step-cycle detection, suffering from cycle detection failures and intercycle phase misalignment. We propose a novel algorithm that avoids both the above two problems. It makes use of a type of salient points termed signature points (SPs), and has three components: 1) a multiscale SP extraction method, including the localization and SP descriptors; 2) a sparse representation scheme for encoding newly emerged SPs with known ones in terms of their descriptors, where the phase propinquity of the SPs in a cluster is leveraged to ensure the physical meaningfulness of the codes; and 3) a classifier for the sparse-code collections associated with the SPs of a series. Experimental results on our publicly available dataset of 175 subjects showed that our algorithm outperformed existing methods, even if the step cycles were perfectly detected for them. When the accelerometers at five different body locations were used together, it achieved the rank-1 accuracy of 95.8% for identification, and the equal error rate of 2.2% for verification. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
21682267
Volume :
45
Issue :
9
Database :
Complementary Index
Journal :
IEEE Transactions on Cybernetics
Publication Type :
Academic Journal
Accession number :
108971048
Full Text :
https://doi.org/10.1109/TCYB.2014.2361287