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.
|Title of host publication||2018 24th International Conference on Pattern Recognition, ICPR 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2018 Nov 26|
|Event||24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China|
Duration: 2018 Aug 20 → 2018 Aug 24
|Name||Proceedings - International Conference on Pattern Recognition|
|Other||24th International Conference on Pattern Recognition, ICPR 2018|
|Period||18/8/20 → 18/8/24|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition