44 research outputs found
PATH: Person Authentication using Trace Histories
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.Comment: 8 pages, 9 figures. Best Paper award at IEEE UEMCON 201
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201
Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances
Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-Time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances