Learning a Policy for Gesture-Based Active Multi-touch Authentication

Peralta, Raquel Torres; Rebguns, Antons; Fasel, Ian; Barnard, Kobus

doi:10.1007/978-3-642-39345-7_7

Cited by 1 publication

(1 citation statement)

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“…(2) This sample size is consistent with the literature in the field of biometric authentication. Within the field of biometric authentication, smaller sample sizes have been used for the preliminary testing of model performance in authenticating device users (e.g., 5-10 participants) [16][17][18][19][20]. (3) The machine learning models used in this study are trained to predict the child user at the 1-s level (i.e., with a high degree of granularity); therefore, the amount of data points per subject is large (see Table S4).…”

Section: Methodsmentioning

confidence: 99%

Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study

Finnegan,

Weaver,

Yang

et al. 2024

Human Behavior and Emerging Technologies

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Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

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