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• W1749247050 abstract "The ever growing number of mobile devices has turned the attention to security and usability. If a mobile device is lost or stolen this can lead to loss of personal information and the possibility of identity theft. People often tend not to use passwords which leads to lack of personal security mainly due to convenience and frequent use. This paper suggests to serve both convenience and security needs at the same time. Thus we suggest to observe the user's gait characteristic. Our approach realizes user authentication by applying the discrete wavelet transform (DWT) to acceleration signals obtained from mobile devices. Gait templates were constructed of Bark-frequency cepstral coefficients (BFCC) from the wavelet coefficients and these were arranged to train a support vector machine (SVM). A cross-day scenario demonstrates that the proposed approach shows competitive recognition performance, yielding 9.82% False Match Rate (FMR) at a False Non-Match Rate (FNMR) of 10.45%." @default.
• W1749247050 created "2016-06-24" @default.
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• W1749247050 date "2012-04-11" @default.
• W1749247050 modified "2023-09-28" @default.
• W1749247050 title "Biometric gait recognition for mobile devices using wavelet transform and support vector machines" @default.
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