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Face-Based Multiple User Active Authentication on Mobile Devices

机译:移动设备上基于面部的多用户主动身份验证

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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.
机译:与单用户主动身份验证相反,多用户主动身份验证要求验证多个主题的身份。传统的验证和基于标识的解决方案都无法解决此问题中提出的特定挑战。我们介绍了极度开放集拒绝,这是一种双重机制,具有基于稀疏表示的识别步骤和为此目的进行的验证步骤。在验证步骤中,稀疏向量的集中以及匹配分布和不匹配分布之间的重叠被考虑用于决策。我们引入了基于极值理论的半参数模型来对分布进行建模,并引入了一种算法来估计极值分布的参数。使用三个公开可用的基于面部的移动主动身份验证数据集证明了该方法的有效性。

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