One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication

Seokmin Choi, Inho Chang, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538637883
Publication statusPublished - 2018 Nov 26
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 2018 Aug 202018 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other24th International Conference on Pattern Recognition, ICPR 2018

Bibliographical note

Funding Information:
ACKNOWLEDGSMENT “This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NO. 2016R1A2B4011656)”

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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