Touch Gesture-Based Active User Authentication Using Dictionaries

Zhang, Heng; Patel, Vishal M.; Fathy, Mohammed E.; Chellappa, Rama

doi:10.1109/wacv.2015.35

Cited by 72 publications

(40 citation statements)

References 12 publications

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“…For instance, data collected in the enrollment phase may differ than those in the recognition phase. The active authentication system should use some domain adaptive method to handle this issue [58]. • Biometric feature extraction and selection:…”

Section: Limitations and Challengesmentioning

confidence: 99%

A survey on behavioral biometric authentication on smartphones

Mahfouz

Mahmoud

Eldin

2017

Journal of Information Security and Applications

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Recent research has shown the possibility of using smartphones' sensors and accessories to extract some behavioral attributes such as touch dynamics, keystroke dynamics and gait recognition. These attributes are known as behavioral biometrics and could be used to verify or identify users implicitly and continuously on smartphones. The authentication systems that have been built based on these behavioral biometric traits are known as active or continuous authentication systems.This paper provides a review of the active authentication systems. We present the components and the operating process of the active authentication systems in general, followed by an overview of the state-of-theart behavioral biometric traits that used to develop an active authentication systems and their evaluation on smartphones. We discuss the issues, strengths and limitations that associated with each behavioral biometric trait. Also, we introduce a comparative summary between them. Finally, challenges and open research problems are presented in this research field.

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Section: Limitations and Challengesmentioning

confidence: 99%

A survey on behavioral biometric authentication on smartphones

Mahfouz

Mahmoud

Eldin

2017

Journal of Information Security and Applications

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“…Active authentication systems deal with this issue by continuously monitoring the user identity after the initial access has been granted. Examples include systems based on screen touch gestures [63], gait recognition [64], and device movement patterns (as measured by the accelerometer) [65]. The development of cancelable active authentication systems is a nascent area of research.…”

Section: Discussionmentioning

confidence: 99%

Cancelable Biometrics: A review

Patel

Ratha

Chellappa

2015

IEEE Signal Process. Mag.

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350

129

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“…To deal with this issue, Active Authentication (AA) systems were introduced in which users are continuously monitored after the initial access to the mobile device [3]. Various methods have been proposed for AA including touch gesture-based [4], [5], [6], [7], gait patternbased [8] and face-based [9], [10], [11], [12], [13] systems. In particular, face-based AA systems have gained a lot of attraction in recent years.…”

Section: Introductionmentioning

confidence: 99%

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Oza

Patel

2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at : github.com/otkupjnoz/oc-acnn.

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Touch Gesture-Based Active User Authentication Using Dictionaries

Cited by 72 publications

References 12 publications

A survey on behavioral biometric authentication on smartphones

A survey on behavioral biometric authentication on smartphones

Cancelable Biometrics: A review

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

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