An Approach for Designing Low Cost Deep Neural Network based Biometric Authentication Model for Smartphone User
Basabi Chakraborty, Kotaro Nakano, Yoshitomo Tokoi, Takako Hashimoto · 2019
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.