Face-Based Multiple User Active Authentication on Mobile Devices

Multiple user active authentications, in contrast with the single user active authentication, require the verification of identity of multiple subjects. Both traditional verification and identification-based solutions fail to address the specific challenges presented in this problem. We introduce Extremal Openset Rejection, a two-fold mechanism with a sparse representation-based identification step and a verification step for this purpose. In the verification step, concentration of the sparsity vector and the overlap between matched and non-matched distributions are considered for decision making. We introduce a semi-parametric model based on Extreme Value Theory for modeling the distributions, and an algorithm to estimate the parameters of extreme value distributions. Effectiveness of the proposed method is demonstrated using three publicly available face-based mobile active authentication data sets.