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Mobile devices can effectively authenticate users via handwritten signatures using deep neural networks. This novel method achieves low error rates for both random and skilled forgeries, enhancing biometric security.

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Area of Science:

  • Biometrics
  • Machine Learning
  • Computer Vision

Background:

  • Biometric authentication is crucial for identity verification.
  • Handwritten signature verification is a challenging biometric modality.
  • Mobile devices offer a convenient platform for biometric data acquisition.

Purpose of the Study:

  • To evaluate the efficacy of mobile-acquired handwritten signatures for user authentication.
  • To introduce a novel online signature verification method utilizing mobile sensor data.
  • To investigate the performance of deep neural networks in signature verification.

Main Methods:

  • Collected online signature data (coordinates, pressure) using a mobile application.
  • Developed and applied convolutional neural network (CNN) models: SigNet, SigNetExt, and VGG-16.
  • Conducted closed-set verification experiments on the MobiBits database subset.

Main Results:

  • Achieved an Equal Error Rate (EER) of 0.63% for random forgeries.
  • Achieved an EER of 6.66% for skilled forgeries.
  • Demonstrated the successful application of deep neural networks for mobile signature verification.

Conclusions:

  • Deep neural network architectures are effective for online handwritten signature verification.
  • Mobile devices are suitable for capturing reliable signature data for authentication.
  • The proposed method offers a promising solution for secure and convenient biometric authentication.