A multimodal data set for evaluating continuous authentication performance in smartphones

Yang, Qing; Ge, Peng; Nguyen, David T.; Qi, Xin; Zhou, Gang; Sitová, Zdeňka; Gasti, Paolo; Balagani, Kiran S.

doi:10.1145/2668332.2668366

Cited by 37 publications

(34 citation statements)

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“…The main data categories used are summarised in Section 5. We have summarised the subject count, whether subjects needed to perform a specific task (everything from walking [35] to navigating maps [14]). Because it takes time to learn a task and that subject's behavioural patterns can change over time, we examine whether researchers collect multiple session data, and if so, the time gap between sessions.…”

Section: Data Usedmentioning

confidence: 99%

“…•accelerometer (used in [43], [14], [45], [35]) one of two sensors that are provided by Android platform to determine the position of a device [55]. Gravity data can be calculated using accelerometer data together with magnetometer and the gyroscope [56].…”

Section: Sensors Used In Behavioural Biometricsmentioning

confidence: 99%

“…We only examine papers, that introduce new datasets. If a paper introduces a dataset but doesn't perform any experiments or the baseline results (as in [14]), we look into the same author successive work that presents such results. Especially, we focus on papers that:…”

Section: Introductionmentioning

confidence: 99%

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Data behind mobile behavioural biometrics – a survey

2020

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Section: Data Usedmentioning

confidence: 99%

Section: Sensors Used In Behavioural Biometricsmentioning

confidence: 99%

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Data behind mobile behavioural biometrics – a survey

2020

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“…To evaluate HMOG features, we used sensor data collected from 100 smartphone users (53 male, 47 female) during eight free text typing sessions [68]. 2 Users answered three questions per session, typing at least 250 characters for each answer.…”

Section: Datasetmentioning

confidence: 99%

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Sitová

Šeděnka

Yang

et al. 2016

IEEE Trans.Inform.Forensic Secur.

Self Cite

329

178

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We introduce Hand Movement, Orientation, and Grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at 16Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions; and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

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“…To explore the nature of inertial sensor signals, we performed a preliminary analysis on the HMOG dataset [33] containing similar data, but collected in constrained settings as a part of a lab study. This data collection was performed with the help of 100 volunteers, each performing 24 sessions of predefined tasks, such as reading, typing and navigation, while sitting or walking.…”

Section: A Visualization: Hmog Datasetmentioning

confidence: 99%

Learning Human Identity From Motion Patterns

Neverova¹,

Wolf²,

Lacey³

et al. 2016

IEEE Access

160

110

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We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind data set of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We compare several neural architectures for efficient learning of temporal multi-modal data representations, propose an optimized shift-invariant dense convolutional mechanism, and incorporate the discriminatively trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems. Finally, we demonstrate that the proposed model can also be successfully applied in a visual context.

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A multimodal data set for evaluating continuous authentication performance in smartphones

Cited by 37 publications

References 1 publication

Data behind mobile behavioural biometrics – a survey

Data behind mobile behavioural biometrics – a survey

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Learning Human Identity From Motion Patterns

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