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Sensors 2018, 18(11), 3872; https://doi.org/10.3390/s18113872

3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor

1
German Research Center for Artificial Intelligence, DFKI, Kaiserslautern 67653, Germany
2
Department of Informatics, University of Kaiserslautern, Kaiserslautern 67653, Germany
3
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
4
University of Prince Mugrin (UPM), Madinah 20012, Saudi Arabia
5
Deep Learning Laboratory, National Center of Artificial Intelligence (NCAI), Islamabad 44000, Pakistan
*
Authors to whom correspondence should be addressed.
Received: 18 October 2018 / Revised: 4 November 2018 / Accepted: 7 November 2018 / Published: 10 November 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
PDF [2226 KB, uploaded 10 November 2018]

Abstract

In-air signature is a new modality which is essential for user authentication and access control in noncontact mode and has been actively studied in recent years. However, it has been treated as a conventional online signature, which is essentially a 2D spatial representation. Notably, this modality bears a lot more potential due to an important hidden depth feature. Existing methods for in-air signature verification neither capture this unique depth feature explicitly nor fully explore its potential in verification. Moreover, these methods are based on heuristic approaches for fingertip or hand palm center detection, which are not feasible in practice. Inspired by the great progress in deep-learning-based hand pose estimation, we propose a real-time in-air signature acquisition method which estimates hand joint positions in 3D using a single depth image. The predicted 3D position of fingertip is recorded for each frame. We present four different implementations of a verification module, which are based on the extracted depth and spatial features. An ablation study was performed to explore the impact of the depth feature in particular. For matching, we employed the most commonly used multidimensional dynamic time warping (MD-DTW) algorithm. We created a new database which contains 600 signatures recorded from 15 different subjects. Extensive evaluations were performed on our database. Our method, called 3DAirSig, achieved an equal error rate (EER) of 0 . 46 %. Experiments showed that depth itself is an important feature, which is sufficient for in-air signature verification. The dataset will be publicly available (https://goo.gl/yFdfdL).
Keywords: in-air signature; depth sensor; convolutional neural network (CNN); 3D hand pose estimation; multidimensional dynamic time warping (MD-DTW) in-air signature; depth sensor; convolutional neural network (CNN); 3D hand pose estimation; multidimensional dynamic time warping (MD-DTW)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Malik, J.; Elhayek, A.; Ahmed, S.; Shafait, F.; Malik, M.I.; Stricker, D. 3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor. Sensors 2018, 18, 3872.

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