One-class random maxout probabilistic network for mobile touchstroke authentication

(presented in 2018 24th International Conference on Pattern Recognition (ICPR)) [Paper link]

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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.

_{[1] S. Choi, I. Chang, and A. B. J. Teoh, “One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication,” in 2018 24th International Conference on Pattern Recognition (ICPR), Aug. 2018, pp. 3359–3364. doi: 10.1109/ICPR.2018.8545451.}