Improvement of on-line signature verification based on gradient features

Yumiko Kawazoe, Wataru Oyama, Tetsushi Wakabayashi, Fumitaka Kimura

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

6 Citations (Scopus)

Abstract

This paper proposes a new on-line signature verification technique which employs gradient features and a pooled within-covariance matrix of training samples not only of the user but also of the others. Gradient features are extracted from a signature image reflecting the velocity of pen movement as the grayscale so that both on-line and off-line features are exploited. All training samples of different signatures collected in design stage are pooled together with the user's samples and used for learning within-individual variation to reduce required sample size of the user to minimum number. The result of evaluation test shows that the proposed technique improves the verification accuracy by 4.9% when user's sample of size three is pooled with samples with others. This result shows that the samples of different signatures are useful for training within-individual variation of a specific user.

Original languageEnglish
Title of host publicationProceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010
Pages410-414
Number of pages5
DOIs
Publication statusPublished - Dec 1 2010
Externally publishedYes
Event12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010 - Kolkata, India
Duration: Nov 16 2010Nov 18 2010

Other

Other12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010
CountryIndia
CityKolkata
Period11/16/1011/18/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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