多元数据融合的非干扰身份识别方法
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摘要
传统的步态识别技术在智能终端设备风险控制领域的应用还存在一些问题,已有的方案是通过加速度、陀螺仪等多传感器对步态进行身份识别与验证.由于现有的识别方法设置了许多限制条件,给该技术的使用与推广俱造成了困难.例如:需要把传感器设备固定在脚踝、膝盖、腰部等位置,设备有指定的朝向,用户做特定的动作.通过步态进行身份识别与验证的技术应用到风险控制领域需要一套完整可靠的系统架构,现有架构还存在较大问题.因此,提出一种与位置、行为无关的非干扰的身份识别与验证方法,该方法仅使用加速度传感器,并以此方法为核心建立了一套完整的系统实现架构,该架构方法的实现提高了系统的整体精度与可用性.首先对用户的行为及设备所在的位置进行预测;然后针对性地进行步态分析与识别.实验中仅使用智能手机中内置的加速度传感器采集数据,最后对步态进行位置无关的分析与识别最重确定用户身份,从而起到降低智能手机使用风险提高安全系数的作用.实验结果表明设计的系统架构有利于系统整体精度的提升,且该方法具有较高的识别率和极低的假阳率(false positive rate, FPR),且在非干扰用户的情况下提高了APP和智能手机等智能终端设备的安全性.
The traditional gait recognition technology in the field of intelligent terminal equipment risk control still has some problems. The existing program is using accelerometer, gyroscope and other multi-sensor gait for identification and verification. Due to the existing identification methods set a number of restrictions, hinder the use and promotion of this technology. For example: the sensor device needs to be fixed at the same position as the ankle, knee, waist and so on; the device has a designated orientation; the user does a specific action. In addition, the application of the technology of identity verification and verification through gait to the field of risk control requires a complete and reliable system architecture. There is still a big problem with the existing architecture. Therefore, this paper presents a non-interference and location-independent identification and verification method that uses only accelerometers and builds a complete set of system implementation architecture with this method as the core. The implementation of this architecture method has improved the overall system accuracy and availability. Firstly, the user's behavior and the location of the device are predicted; then the gait analysis and identification are carried out. In this experiment, we only use the built-in accelerometer in the smart phone to collect data, finally position-independent gait analysis and identification to identify the user to determine which is the most important, so as to reduce the risk of using smart phones and improve the safety factor. The experimental results show that the system architecture designed in this paper is conducive to the improvement of overall system accuracy. The method has the characteristics of high recognition rate and very low FPR(false positive rate), and improves the APP and the smartphone in the case of non-interfering users such as intelligent terminal equipment security.
The traditional gait recognition technology in the field of intelligent terminal equipment risk control still has some problems. The existing program is using accelerometer, gyroscope and other multi-sensor gait for identification and verification. Due to the existing identification methods set a number of restrictions, hinder the use and promotion of this technology. For example: the sensor device needs to be fixed at the same position as the ankle, knee, waist and so on; the device has a designated orientation; the user does a specific action. In addition, the application of the technology of identity verification and verification through gait to the field of risk control requires a complete and reliable system architecture. There is still a big problem with the existing architecture. Therefore, this paper presents a non-interference and location-independent identification and verification method that uses only accelerometers and builds a complete set of system implementation architecture with this method as the core. The implementation of this architecture method has improved the overall system accuracy and availability. Firstly, the user's behavior and the location of the device are predicted; then the gait analysis and identification are carried out. In this experiment, we only use the built-in accelerometer in the smart phone to collect data, finally position-independent gait analysis and identification to identify the user to determine which is the most important, so as to reduce the risk of using smart phones and improve the safety factor. The experimental results show that the system architecture designed in this paper is conducive to the improvement of overall system accuracy. The method has the characteristics of high recognition rate and very low FPR(false positive rate), and improves the APP and the smartphone in the case of non-interfering users such as intelligent terminal equipment security.
引文
[1]Wang Chendi. Alibaba Internet financial risk control measures [D]. Urumchi: Xinjiang University of Finance and Economics, 2016 (in Chinese)(王晨迪. 阿里巴巴互联网金融风险控制措施研究[D]. 乌鲁木齐: 新疆财经大学, 2016)
[2]Zhang Ping. Tencent mobile payment how to do risk control [J]. Gold Card Project, 2015(8): 8- 9 (in Chinese)(张平. 腾讯移动支付如何做风控[J]. 金卡工程, 2015(8): 8- 9)
[3]Mayagoitia R E, Nene A V, Veltink P H. Accelerometer and rate gyroscope measurement of kinematics: An inexpensive alternative to optical motion analysis systems [J]. Journal of Biomechanics, 2002, 35(4): 537- 542
[4]Nishiguchi S, Yamada M, Nagai K, et al. Reliability and validity of gait analysis by android-based smartphone [J]. Telemedicine and e -Health, 2012, 18(4): 292- 296
[5]Shan Yanhu, Zhang Zhang, Huang Kaiqi. Visual human action recognition: History, status and prospects [J]. Journal of Computer Research and Development, 2016, 53(1): 93- 112 (in Chinese)(单言虎, 张彰, 黄凯奇. 人的视觉行为识别研究回顾、 现状及展望[J]. 计算机研究与发展, 2016, 53(1): 93- 112)
[6]Isho T, Tashiro H, Usuda S. Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke [J]. Journal of Stroke and Cerebrovascular Diseases, 2015, 24(6): 1305- 1311
[7]Liu Rong, Zhou Jianzhong, Liu Ming, et al. A wearable acceleration sensor system for gait recognition [C] //Proc of the 2nd IEEE Conf on Industrial Electronics and Applications. Piscataway, NJ: IEEE, 2007: 2654- 2659
[8]Chen Shi, Ma Tianjun, Gao Youxing. Gait recognition using distributions of silhouette feature [J]. Journal of Computer Research and Development, 2009, 46(2): 295- 301 (in Chinese)(陈实, 马天骏, 高有行. 用行人轮廓的分布直方图分类和识别步态 [J]. 计算机研究与发展, 2009, 46(2): 295- 301)
[9]Mantyjarvi J, Lindholm M, Vildjiounaite E, et al. Identifying users of portable devices from gait pattern with accelerometers [C] //Proc of the 5th IEEE Int Conf on Acoustics, Speech, and Signal Processing. Piscataway, NJ: IEEE, 2005: 973- 976
[10]Chen Changyou, Zhang Junping. An iterative gait prototype learning algorithm based on tangent distance [J]. Journal of Computer Research and Development, 2008, 45(7): 1177- 1182 (in Chinese)(陈昌由, 张军平. 基于迭代切距离原型学习算法的步态识别 [J]. 计算机研究与发展, 2008, 45(7): 1177- 1182
[11]Yang Yingchang. Metod and continuously wearable noninvasive apparatus for automatically detecting a stroke and other abnormal health conditions: US, US20140276123[P]. 2014-09-18
[12]Huang Guangbin, Zhu Qinyu, Siew C K. Extreme learning machine: A new learning scheme of feedforward neural networks [C] //Proc of the IEEE Int Joint Conf on Neural Networks. Piscataway, NJ: IEEE, 2004: 985- 990
[13]Tong Kaiyu, Granat M H. A practical gait analysis system using gyroscopes [J]. Medical Engineering & Physics, 1999, 21(2): 87- 94
[14]Lisetti C L, Nasoz F. Using noninvasive wearable computers to recognize human emotions from physiological signals [J]. EURASIP Journal on Advances in Signal Processing, 2004(11): 1672- 1687
[15]Hayfron-Acquah J, Nixon M, Carter J. Automatic gait recognition by symmetry analysis[C] //Proc of the 3rd Int Conf on Audio and Video-Based Biometric Person Authenti-cation. Berlin: Springer, 2001: 272- 277
[16]Mayagoitia R E, Nene A V, Veltink P H. Accelerometer and rate gyroscope measurement of kinematics: An inexpensive alternative to optical motion analysis systems [J]. Journal of Biomechanics, 2002, 35(4): 537- 542
[17]Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al. Extreme learning machine for regression and multiclass classification [J]. IEEE Transactions on Systems Man & Cybernetics: Part B, 2012, 42(2): 513- 529
[18]Han Ju, Bhanu B. Individual recognition using gait energy image [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(2): 316- 322
[19]Arora P, Srivastava S. Gait recognition using gait Gaussian image[C] //Proc of the 2nd Int Conf on Signal Processing and Integrated Networks. Piscataway, NJ: IEEE, 2015: 791- 794
[20]Liu Rong, Duan Zhiguo, Zhou Jianzhong, et al. Identification of individual walking patterns using gait acceleration [C] //Proc of the 1st Int Conf on Bioinformatics and Biomedical Engineering. Piscataway, NJ: IEEE, 2007: 543- 546
[21]Raziff A R A, Sulaiman M N, Mustapha N, et al. Single classifier, OvO, OvA and RCC multiclass classification method in handheld based smartphone gait identification[C] //Proc of the 5th Int Conf on Applied Science and Technology. New York: AIP Publishing: 2017
[2]Zhang Ping. Tencent mobile payment how to do risk control [J]. Gold Card Project, 2015(8): 8- 9 (in Chinese)(张平. 腾讯移动支付如何做风控[J]. 金卡工程, 2015(8): 8- 9)
[3]Mayagoitia R E, Nene A V, Veltink P H. Accelerometer and rate gyroscope measurement of kinematics: An inexpensive alternative to optical motion analysis systems [J]. Journal of Biomechanics, 2002, 35(4): 537- 542
[4]Nishiguchi S, Yamada M, Nagai K, et al. Reliability and validity of gait analysis by android-based smartphone [J]. Telemedicine and e -Health, 2012, 18(4): 292- 296
[5]Shan Yanhu, Zhang Zhang, Huang Kaiqi. Visual human action recognition: History, status and prospects [J]. Journal of Computer Research and Development, 2016, 53(1): 93- 112 (in Chinese)(单言虎, 张彰, 黄凯奇. 人的视觉行为识别研究回顾、 现状及展望[J]. 计算机研究与发展, 2016, 53(1): 93- 112)
[6]Isho T, Tashiro H, Usuda S. Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke [J]. Journal of Stroke and Cerebrovascular Diseases, 2015, 24(6): 1305- 1311
[7]Liu Rong, Zhou Jianzhong, Liu Ming, et al. A wearable acceleration sensor system for gait recognition [C] //Proc of the 2nd IEEE Conf on Industrial Electronics and Applications. Piscataway, NJ: IEEE, 2007: 2654- 2659
[8]Chen Shi, Ma Tianjun, Gao Youxing. Gait recognition using distributions of silhouette feature [J]. Journal of Computer Research and Development, 2009, 46(2): 295- 301 (in Chinese)(陈实, 马天骏, 高有行. 用行人轮廓的分布直方图分类和识别步态 [J]. 计算机研究与发展, 2009, 46(2): 295- 301)
[9]Mantyjarvi J, Lindholm M, Vildjiounaite E, et al. Identifying users of portable devices from gait pattern with accelerometers [C] //Proc of the 5th IEEE Int Conf on Acoustics, Speech, and Signal Processing. Piscataway, NJ: IEEE, 2005: 973- 976
[10]Chen Changyou, Zhang Junping. An iterative gait prototype learning algorithm based on tangent distance [J]. Journal of Computer Research and Development, 2008, 45(7): 1177- 1182 (in Chinese)(陈昌由, 张军平. 基于迭代切距离原型学习算法的步态识别 [J]. 计算机研究与发展, 2008, 45(7): 1177- 1182
[11]Yang Yingchang. Metod and continuously wearable noninvasive apparatus for automatically detecting a stroke and other abnormal health conditions: US, US20140276123[P]. 2014-09-18
[12]Huang Guangbin, Zhu Qinyu, Siew C K. Extreme learning machine: A new learning scheme of feedforward neural networks [C] //Proc of the IEEE Int Joint Conf on Neural Networks. Piscataway, NJ: IEEE, 2004: 985- 990
[13]Tong Kaiyu, Granat M H. A practical gait analysis system using gyroscopes [J]. Medical Engineering & Physics, 1999, 21(2): 87- 94
[14]Lisetti C L, Nasoz F. Using noninvasive wearable computers to recognize human emotions from physiological signals [J]. EURASIP Journal on Advances in Signal Processing, 2004(11): 1672- 1687
[15]Hayfron-Acquah J, Nixon M, Carter J. Automatic gait recognition by symmetry analysis[C] //Proc of the 3rd Int Conf on Audio and Video-Based Biometric Person Authenti-cation. Berlin: Springer, 2001: 272- 277
[16]Mayagoitia R E, Nene A V, Veltink P H. Accelerometer and rate gyroscope measurement of kinematics: An inexpensive alternative to optical motion analysis systems [J]. Journal of Biomechanics, 2002, 35(4): 537- 542
[17]Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al. Extreme learning machine for regression and multiclass classification [J]. IEEE Transactions on Systems Man & Cybernetics: Part B, 2012, 42(2): 513- 529
[18]Han Ju, Bhanu B. Individual recognition using gait energy image [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(2): 316- 322
[19]Arora P, Srivastava S. Gait recognition using gait Gaussian image[C] //Proc of the 2nd Int Conf on Signal Processing and Integrated Networks. Piscataway, NJ: IEEE, 2015: 791- 794
[20]Liu Rong, Duan Zhiguo, Zhou Jianzhong, et al. Identification of individual walking patterns using gait acceleration [C] //Proc of the 1st Int Conf on Bioinformatics and Biomedical Engineering. Piscataway, NJ: IEEE, 2007: 543- 546
[21]Raziff A R A, Sulaiman M N, Mustapha N, et al. Single classifier, OvO, OvA and RCC multiclass classification method in handheld based smartphone gait identification[C] //Proc of the 5th Int Conf on Applied Science and Technology. New York: AIP Publishing: 2017