Touch-based Smartphone Authentication Using Import Vector Domain Description
Bin Zou, Yantao Li · 2018
The security of smartphones is vital as much privacy information is stored on them. In this paper, we propose a touch-based authentication system by exploiting a novel one-class classification algorithm import vector domain description (IVDD). We record each complete stroke by a 32-dimensional attribute vector and select features with high discriminability by employing the correlation analysis and the conditional mutual information maximization. With the selected features, the trained IVDD classifier generates a probabilistic result for each user, which is further used for authentication based on a predefined threshold. We evaluate the classifier IVDD in terms of the impact of the number of touch behaviors, computation time, individual effect, and accuracy, and the experimental results show that the IVDD reaches the lower BER of 2.5% under 15 touches and lower mean FRR of 2.14%, comparing with the support vector domain description (SVDD).