PATH: Person Authentication using Trace Histories
This work addresses active authentication for mobile device security, but it is incremental as it builds on existing HMM approaches with smoothing techniques.
The paper tackles user verification on mobile devices by modeling location trace histories as Markovian motion and proposes a Marginally Smoothed HMM (MSHMM) that outperforms baseline methods, achieving a lower equal error rate (EER) on datasets like UMDAA02 and GeoLife.
In this paper, a solution to the problem of Active Authentication using trace histories is addressed. Specifically, the task is to perform user verification on mobile devices using historical location traces of the user as a function of time. Considering the movement of a human as a Markovian motion, a modified Hidden Markov Model (HMM)-based solution is proposed. The proposed method, namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities of location and timing information of the observations to smooth-out the emission probabilities while training. Hence, it can efficiently handle unforeseen observations during the test phase. The verification performance of this method is compared to a sequence matching (SM) method , a Markov Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap). Experimental results using the location information of the UMD Active Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented. The proposed MSHMM method outperforms the compared methods in terms of equal error rate (EER). Additionally, the effects of different parameters on the proposed method are discussed.