Learning the micro deformations by max-pooling for offline signature verification

Yuchen Zheng, Brian Kenji Iwana, Muhammad Imran Malik, Sheraz Ahmed, Wataru Oyama, Seiichi Uchida

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

For signature verification systems, micro deformations can be defined as the small differences in the same strokes of signatures or special writing habits of different signers. These micro deformations can reveal the core distinction between the genuine signatures and skilled forgeries. In this paper, we prove that Convolutional Neural Networks (CNNs) have the potential to extract those micro deformations by max-pooling. More specifically, the micro deformations can be determined by watching the location coordinates of the maximum values in pooling windows of max-pooling. Extensive analysis and experiments demonstrate that it is possible to achieve state-of-the-art performance by using this location information as a new feature for capturing micro deformations, along with convolutional features. The proposed method outperforms the state-of-the-art systems on four publicly available datasets of different languages, i.e., English (GPDSsynthetic, CEDAR), Persian (UTSig), and Hindi (BHSig260).

Original languageEnglish
Article number108008
JournalPattern Recognition
Volume118
DOIs
Publication statusPublished - Oct 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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