An Approach for Designing Low Cost Deep Neural Network based Biometric Authentication Model for Smartphone User
An Approach for Designing Low Cost Deep Neural Network based Biometric Authentication Model for Smartphone User
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DOI：
10.1109/tencon.2019.8929241
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
B. Chakraborty;Kotaro Nakano;Yoshitomo Tokoi;T. Hashimoto
中科院分区：
文献类型：
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作者：
B. Chakraborty;Kotaro Nakano;Yoshitomo Tokoi;T. Hashimoto
随着智能手机的使用越来越多，已经开发了许多基于智能手机的应用程序。智能手机用于老年人的个人保健或监测活动。这些类型的智能电话应用需要用户的连续认证，以便在由于遗忘或盗窃而使智能电话从用户分离的情况下采取行动。智能手机上的连续认证要求认证过程具有低计算开销。在这项工作中，目标是开发低成本的用户身份验证算法从时间序列数据的用户活动从传感器，如加速度计或陀螺仪。深度神经网络用于用户身份验证。一个两步的认证过程中，传感器数据已被第一次分类到不同的活动和活动相关的认证提出。为了降低分类器的计算成本，采用知识蒸馏的方法来降低模型参数。微调用于科普有限数量的训练数据。其结果是，认证精度提高了5%到10%，认证时间也达到了0.032秒，这对于真实的时间认证是有用的。仿真研究已经做了几个基准数据集，以评估所提出的方法的效率。
With the increasing use of smartphones, lots of smartphone based applications have been developed. Smart-phones are used in personal health care or monitoring activities of elderly persons. These types of smartphone applications require continuous authentication of the user for taking action in case of detachment of the smartphone from the user due to forgetfulness or theft. Continuous authentication on smartphone requires authentication process having low computational overhead. In this work, the objective is to develop low cost user authentication algorithm from time series data of user activities taken from sensors like accelerometer or gyroscope. Deep neural networks are used for user authentication. A two-step authentication process has been developed in which sensor data has been first classified into different activities and activity dependent authentication is proposed. For lowering computational cost of classifier, knowledge distillation is used to reduce the model parameters. Fine tuning is used to cope with the limited number of training data. As a result the authentication accuracy has been improved by 5% to 10%, also authentication time of 0.032 sec has been achieved which is useful for real time authentication. Simulation studies have been done by several bench mark data sets to evaluate the efficiency of the proposed approach.