AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns
Marta Robledo-Moreno, Ruben Vera-Rodriguez, Ruben Tolosana, Javier Ortega-Garcia, Andres Huergo, Julian Fierrez

TL;DR
This paper introduces AirSignatureDB, a comprehensive dataset for in-air signature biometrics, benchmarks verification methods, and explores privacy concerns by reconstructing signature trajectories from sensor data.
Contribution
It provides the first large-scale, real-world dataset for in-air signatures, benchmarks verification techniques, and demonstrates trajectory reconstruction from inertial sensors, raising privacy issues.
Findings
Reconstructed 3D signature trajectories from sensor data.
Benchmarking shows deep learning improves verification accuracy.
Trajectory reconstruction challenges the assumption of traceless in-air gestures.
Abstract
Behavioral biometrics based on smartphone motion sensors are growing in popularity for authentication purposes. In this study, AirSignatureDB is presented: a new publicly accessible dataset of in-air signatures collected from 108 participants under real-world conditions, using 83 different smartphone models across four sessions. This dataset includes genuine samples and skilled forgeries, enabling a comprehensive evaluation of system robustness against realistic attack scenarios. Traditional and deep learning-based methods for in-air signature verification are benchmarked, while analyzing the influence of sensor modality and enrollment strategies. Beyond verification, a first approach to reconstructing the three-dimensional trajectory of in-air signatures from inertial sensor data alone is introduced. Using on-line handwritten signatures as a reference, we demonstrate that the recovery…
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Taxonomy
TopicsUser Authentication and Security Systems · Biometric Identification and Security · Gait Recognition and Analysis
