ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (3): 635-642.doi: 10.7544/issn1000-1239.2019.20170807

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Multi-Model Data Fusion Based Unobtrusive Identification Method

Yu Diancun1,2, Chen Yiqiang1,3, Peng Xiaohui1, Jiao Shuai1, Li Xiaohai1, Zhong Xi1,3   

  1. 1(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190); 2(School of Software, Shandong University, Jinan 250101); 3(University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2019-03-01

Abstract: 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.

Key words: human-computer interaction, safety, gait, risk control, noninterference

CLC Number: