One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication

Continuous authentication (CA) with touch stroke dynamics is an emerging problem for mobile identity management. In this paper, we focus on one of the essential problems in CA namely one-class classification problem. We propose a novel analytic probabilistic one-class classifier coined One-Class Random MaxOut Probabilistic Network (OC-RMPNet). The OC-RMPNet is a single hidden layer network that is tailored to capture individual users' touch-stroke profiles. The input-hidden layer of the network is meant to project the input vector onto the high dimensional random maxout feature space and the hidden-output layer acts as an OC probabilistic predictor that trained by means of least-square principle, hence require no iterative learning. We also put forward a feature sequential fusion mechanism for accuracy improvement. We scrutinize and compare the proposed methods with existing works on touchanalytics and HMOG datasets. The empirical results reveal that the OC-RMPNet prevails over its predecessor in touch-stroke authentication tasks on mobile phones.