Prewarping Siamese Network: Learning Local Representations for Online Signature Verification

Xiaomeng Wu, Akisato Kimura, Seiichi Uchida, Kunio Kashino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

We propose a neural network-based framework for learning local representations of multivariate time series, and demonstrate its effectiveness for online signature verification. In contrast to related works that optimize a global distance objective, we incorporate a Siamese network into dynamic time warping (DTW), leading to a novel prewarping Siamese network (PSN) optimized with a local embedding loss. PSN learns a feature space that preserves the temporal location-wise distances of local structures. Local embedding, along with the alignment conditions of DTW, imposes a temporal consistency constraint on the sequence-level distance measure while achieving invariance as regards non-linear distortions. Validation on online signature verification datasets demonstrates the advantage of our framework over existing techniques that use either handcrafted or learned feature representations.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2467-2471
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period5/12/195/17/19

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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