Toward a non-intrusive, physio- behavioral biometric for smartphones

Vasiete, Esther; Chen, Yan; Char, Ian; Yeh, Tom; Patel, Vishal M.; Davis, Larry S.; Chellappa, Rama

doi:10.1145/2628363.2634223

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Mobile nodes constitute a kind of typical dynamic complex network in the process of pervasive sensing service [34]. The social relations as the product of the activity reflect the relationship between the active subjects and reveal the intrinsic nature of the mobile nodes [35]. As the link between different mobile nodes, the direction and weight value of the social relationship usually contain very important social information and can truly reflect the close degree of connections and trust relationship in real nodes.…”

Section: Community Discovery Based On Directed Weighted Mechanismmentioning

confidence: 99%

Research on social network discovery algorithm in pervasive sensing environment

Gao

Zhu

Gong

et al. 2016

Concurrency and Computation

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SUMMARYConsidering the real social community network partition approach regardless of the directed and weighted characteristic, we propose a novel algorithm in pervasive sensing environment. The proposed SDOR algorithm is based on the definition of nodes optimal route, community likeness index, community discrete degree index and so on parameters to achieve the sensible partition for directed weighted social community network. We conduct some different types of experiments to verify the scalability, accuracy, and validity of the proposed algorithm. The experimental consequences demonstrate that the accuracy of the algorithm is about 90%, higher than existing models about 10%. In addition, the algorithm has good effectiveness and scalability in other different kinds of network.

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Section: Community Discovery Based On Directed Weighted Mechanismmentioning

confidence: 99%

Research on social network discovery algorithm in pervasive sensing environment

Gao

Zhu

Gong

et al. 2016

Concurrency and Computation

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“…References [13][14][15][16] are some researches about smartphone identity authentication by means of physiological characteristics. In [13], the authors use multiple biometrics such as voice, face, and signature data for authentication on smartphones.…”

Section: Related Workmentioning

confidence: 99%

An Implicit Identity Authentication System Considering Changes of Gesture Based on Keystroke Behaviors

Chen

2015

International Journal of Distributed Sensor Networks

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Smartphones have become ubiquitous personal devices so that much of sensitive and private information will be saved in the phone, and users have their own unique behavioral characteristics when using smartphones, so, to prevent private information from falling into the hands of impostors, there is a kind of identity authentication system based on user's behavioral features while the user is unlocking. However, due to the impact of environmental factors, changes of gesture will introduce bias into the feature data, which results in a diminishment of the system performance. To solve this problem, we propose an implicit identity authentication system based on keystroke behaviors, and it is the first attempt to consider the changes of a user's gesture. This system collects five keystroke features in the background and analyzes to identify different users without additional hardware supporting. We present our work with an experimental study, and our experiments show that the accuracy of identity authentication system we proposed is up to 99.1329%. Comparing with the identity authentication system without considering the impact of gesture changes, the EER of the system considering the impact of gesture changes is decreased by 1.2514%.

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