计算机应用 ›› 2019, Vol. 39 ›› Issue (6): 1747-1752.DOI: 10.11772/j.issn.1001-9081.2018102161

• 网络空间安全 • 上一篇    下一篇

基于智能手机运动传感器的步态特征身份识别方法

孔菁, 郭渊博, 刘春辉, 王一丰   

  1. 信息工程大学, 郑州 450001
  • 收稿日期:2018-10-26 修回日期:2018-12-26 发布日期:2019-06-17 出版日期:2019-06-10
  • 通讯作者: 孔菁
  • 作者简介:孔菁(1993-),女,辽宁营口人,硕士,主要研究方向:网络安全、异常检测;郭渊博(1975-),男,陕西周至人,教授,博士生导师,博士,主要研究方向:大数据安全、态势感知;刘春辉(1990-),男,山东安丘人,硕士,主要研究方向:网络安全、用户画像;王一丰(1994-),男,江苏泰兴人,硕士研究生,主要研究方向:多步网络安全、深度学习。
  • 基金资助:
    国家自然科学基金资助项目(61602515,61501515)。

Gait feature identification method based on motion sensor in smartphone

KONG Jing, GUO Yuanbo, LIU Chunhui, WANG Yifeng   

  1. Information Engineering University, Zhengzhou Henan 450001, China
  • Received:2018-10-26 Revised:2018-12-26 Online:2019-06-17 Published:2019-06-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61602515, 61501515).

摘要: 利用行为特征进行身份验证是生物识别的前沿技术。为优化基于步态特征的身份识别研究中对数据的处理并改进识别的方式,提出利用智能手机运动传感器数据提取步态特征用于身份识别的方法。首先,应用空间转换算法解决传感器坐标系漂移问题,使数据可以完整准确地刻画行为特征;然后,利用支持向量机(SVM)算法对用户切换所导致的步态特征变化进行分类识别。实验结果表明,经过欧拉角法处理后,所提方法识别准确率达到95.5%,在有效识别用户变换的同时降低了空间开销和实现难度。

关键词: 空间坐标转换, 步态特征, 加速度传感器, 欧拉角法, 支持向量机

Abstract: The identification based on behavior features is a leading technology of biometric recognition. In order to optimize the process of data processing and the way of recognition in the existing studies of identification based on gait feature, a method of extracting gait features from the data of smart phone motion sensors for identification was proposed. Firstly, a spatial transformation algorithm was used to solve the problem of sensor coordinate system drift, making the data to describe the behavior features completely and accurately. Then, Support Vector Machine (SVM) algorithm was used to classify and identify gait features change caused by user transformation. The experimental results show that, the identification accuracy of the proposed method is 95.5%. It can be used to effectively identify user transformation with reduction of space cost and implementation difficulty.

Key words: spatial coordinate transformation, gait feature, acceleration sensor, Euler angle method, Support Vector Machine (SVM)

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