Rapid off-line signature verification based on signature envelope and adaptive density partitioning

Vahid Malekian, Alireza Aghaei, Mahdie Rezaeian, Mahmood Alian

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

2 Citations (Scopus)

Abstract

Handwritten signature is a widely used biometric which incorporates high intra personal variance. The most challenging problem in automatic signature verification is to extract features which are robust against this natural variability and at the same time discriminate between genuine and fake samples. This paper presents a novel method for extracting easily computed rotation and scale invariant features for offline signature verification. These features are extracted using the signature envelope and adaptive density partitioning. The effectiveness of the proposed features has been investigated over 900 signatures using a neural network classifier. The experimental results show the verification accuracy rate of 90.7%.

Original languageEnglish
Title of host publication2013 First Iranian Conference on Pattern Recognition and Image Analysis
Subtitle of host publicationPRIA 2013
Place of PublicationPiscataway, NJ
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event1st Iranian Conference on Pattern Recognition and Image Analysis, PRIA 2013 - Birjand, Iran, Islamic Republic of
Duration: 6 Mar 20138 Mar 2013

Other

Other1st Iranian Conference on Pattern Recognition and Image Analysis, PRIA 2013
CountryIran, Islamic Republic of
CityBirjand
Period6/03/138/03/13

Keywords

  • Adaptive Density Partitioning
  • Artificial Neural Network (ANN)
  • Offline Signature Verification
  • Signature Envelope

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