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

Jameel Malik1,2,3, Ahmed Elhayek4, Sheraz Ahmed5

  • 1German Research Center for Artificial Intelligence, DFKI, Kaiserslautern 67653, Germany. jameel.malik@dfki.de.

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|November 15, 2018
PubMed
Summary

In-air signature verification can now utilize a hidden depth feature for improved accuracy. This new 3D approach significantly enhances user authentication and access control systems.

Keywords:
3D hand pose estimationconvolutional neural network (CNN)depth sensorin-air signaturemultidimensional dynamic time warping (MD-DTW)

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Area of Science:

  • Computer Vision
  • Biometrics
  • Human-Computer Interaction

Background:

  • In-air signatures are crucial for contactless user authentication but are often treated as 2D data, neglecting their depth potential.
  • Existing verification methods lack explicit depth feature capture and rely on unreliable heuristic detection.
  • Deep learning advancements in hand pose estimation offer new possibilities for 3D in-air signature analysis.

Purpose of the Study:

  • To propose a novel real-time in-air signature acquisition method using 3D hand joint position estimation from a single depth image.
  • To develop and evaluate verification modules leveraging both depth and spatial features for enhanced accuracy.
  • To investigate the significance of the depth feature in in-air signature verification.

Main Methods:

  • Real-time 3D hand joint position estimation from a single depth image using deep learning.
  • Recording the 3D fingertip position for each frame of the in-air signature.
  • Implementing four verification modules based on extracted depth and spatial features.
  • Utilizing the multidimensional dynamic time warping (MD-DTW) algorithm for signature matching.
  • Creating and evaluating on a new database of 600 signatures from 15 subjects.

Main Results:

  • The proposed 3DAirSig method achieved a highly competitive equal error rate (EER) of 0.46% on the new database.
  • Ablation studies confirmed that depth information is a critical and sufficient feature for effective in-air signature verification.
  • The developed method demonstrates real-time performance in acquiring and verifying in-air signatures.

Conclusions:

  • The 3D depth feature significantly enhances in-air signature verification accuracy compared to traditional 2D methods.
  • The proposed 3DAirSig method offers a robust and accurate solution for contactless user authentication.
  • The publicly available dataset will facilitate further research in 3D in-air signature analysis.