特征步:步态识别的巨大飞跃

2010 2nd International Workshop on Security and Communication Networks (IWSCN) Pub Date : 2010-05-26 DOI:10.1109/IWSCN.2010.5497991

Patrick A. H. Bours, R. Shrestha

{"title":"特征步:步态识别的巨大飞跃","authors":"Patrick A. H. Bours, R. Shrestha","doi":"10.1109/IWSCN.2010.5497991","DOIUrl":null,"url":null,"abstract":"In this paper we will show that using Principle Component Analysis (PCA) on accelerometer based gait data will give a large improvement on the performance. On a dataset of 720 gait samples (60 volunteers and 12 gait samples per volunteer) we achieved an EER of 1.6% while the best result so far, using the Average Cycle Method (ACM), gave a result of nearly 6%. This tremendous increase makes gait recognition a viable method in commercial applications in the near future.","PeriodicalId":217163,"journal":{"name":"2010 2nd International Workshop on Security and Communication Networks (IWSCN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"Eigensteps: A giant leap for gait recognition\",\"authors\":\"Patrick A. H. Bours, R. Shrestha\",\"doi\":\"10.1109/IWSCN.2010.5497991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we will show that using Principle Component Analysis (PCA) on accelerometer based gait data will give a large improvement on the performance. On a dataset of 720 gait samples (60 volunteers and 12 gait samples per volunteer) we achieved an EER of 1.6% while the best result so far, using the Average Cycle Method (ACM), gave a result of nearly 6%. This tremendous increase makes gait recognition a viable method in commercial applications in the near future.\",\"PeriodicalId\":217163,\"journal\":{\"name\":\"2010 2nd International Workshop on Security and Communication Networks (IWSCN)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Security and Communication Networks (IWSCN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWSCN.2010.5497991\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Workshop on Security and Communication Networks (IWSCN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWSCN.2010.5497991","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 42

摘要

在本文中，我们将证明在基于加速度计的步态数据上使用主成分分析(PCA)将大大提高性能。在720个步态样本的数据集(60名志愿者和每个志愿者12个步态样本)上，我们实现了1.6%的EER，而迄今为止使用平均周期方法(ACM)的最佳结果给出了接近6%的结果。这种巨大的增长使步态识别在不久的将来成为商业应用的可行方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Eigensteps: A giant leap for gait recognition

In this paper we will show that using Principle Component Analysis (PCA) on accelerometer based gait data will give a large improvement on the performance. On a dataset of 720 gait samples (60 volunteers and 12 gait samples per volunteer) we achieved an EER of 1.6% while the best result so far, using the Average Cycle Method (ACM), gave a result of nearly 6%. This tremendous increase makes gait recognition a viable method in commercial applications in the near future.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2010 2nd International Workshop on Security and Communication Networks (IWSCN)

自引率

0.00%

发文量