步态识别的预测模型

S. Enokida, R. Shimomoto, T. Wada, T. Ejima
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引用次数: 8

摘要

步态识别作为一种非接触式、不显眼的生物识别方法,一直受到人们的关注。正常行走时踝关节水平和垂直运动的幅值和相位谱是步态识别的有效特征。然而,由于服装、表面或时间推移等协变量引起的方差,步态识别率明显下降。为了提高对多种鞋类的步态识别率,本文提出了一种预测模型。该预测模型能够从鞋的步态中估计出拖鞋的步态。利用预测步态模型对拖鞋步态随时间变化的识别率比不使用预测模型的识别率高得多。本文设计的预测模型成功地分离了鞋类协变量方差和时间协变量方差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Predictive Model for Gait Recognition
Gait Recognition has been paid an attention to as non-contact and unobtrusive biometric method. Magnitude and phase spectra of horizontal and vertical movement of ankles in a normal walk are effective and efficient signatures in gait recognition. However, gait recognition rate degrades significantly due to variance caused by covariates of clothing, surface or time lapse. In this paper, to improve gait recognition rate on a variety of footwear, a predictive model is proposed. The predictive model is able to estimate slipper gait from shoes gait. By using predictive slipper gait, much higher recognition rate is achieved for slipper gait over time lapse than ones without predictive model. The predictive model designed in this paper succeeds in separation of the variance due to a footwear covariate from the variance due to a time covariate.
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