One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication

Seokmin Choi, Inho Chang, Beng Jin Teoh

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

1 Citation (Scopus)

Abstract

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.
Pages3359-3364
Number of pages6
ISBN (Electronic)9781538637883
DOIs
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
Volume2018-August
ISSN (Print)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period18/8/2018/8/24

Fingerprint

Authentication
Network layers
Mobile phones
Classifiers
Fusion reactions

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Choi, S., Chang, I., & Teoh, B. J. (2018). One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication. In 2018 24th International Conference on Pattern Recognition, ICPR 2018 (pp. 3359-3364). [8545451] (Proceedings - International Conference on Pattern Recognition; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8545451
Choi, Seokmin ; Chang, Inho ; Teoh, Beng Jin. / One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication. 2018 24th International Conference on Pattern Recognition, ICPR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3359-3364 (Proceedings - International Conference on Pattern Recognition).
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abstract = "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.",
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Choi, S, Chang, I & Teoh, BJ 2018, One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication. in 2018 24th International Conference on Pattern Recognition, ICPR 2018., 8545451, Proceedings - International Conference on Pattern Recognition, vol. 2018-August, Institute of Electrical and Electronics Engineers Inc., pp. 3359-3364, 24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 18/8/20. https://doi.org/10.1109/ICPR.2018.8545451

One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication. / Choi, Seokmin; Chang, Inho; Teoh, Beng Jin.

2018 24th International Conference on Pattern Recognition, ICPR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3359-3364 8545451 (Proceedings - International Conference on Pattern Recognition; Vol. 2018-August).

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

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Choi S, Chang I, Teoh BJ. One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication. In 2018 24th International Conference on Pattern Recognition, ICPR 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3359-3364. 8545451. (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2018.8545451