Learning Local Correspondences for Static Signature Verification

  • Authors:
  • G. Pirlo;D. Impedovo;E. Stasolla;C. A. Trullo

  • Affiliations:
  • Dipartimento di Informatica, Università degli Studi di Bari, Bari 70126 and Centro "Rete Puglia", Università degli Studi di Bari, Bari 70100;Dip. di Ing. Elettrotecnica ed Elettronica, Politecnico di Bari, Bari 70126 and Centro "Rete Puglia", Università degli Studi di Bari, Bari 70100;Dipartimento di Informatica, Università degli Studi di Bari, Bari 70126 and Centro "Rete Puglia", Università degli Studi di Bari, Bari 70100;Dipartimento di Informatica, Università degli Studi di Bari, Bari 70126 and Centro "Rete Puglia", Università degli Studi di Bari, Bari 70100

  • Venue:
  • AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
  • Year:
  • 2009

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Abstract

This paper presents a new approach for off-line signature verification. Signature verification is performed by matching only well-selected regions of the signature images. More precisely, from the analysis of lower and upper contours of a signature image, region stability is estimated and the most stable regions are selected for verification, during the enrollment phase. In the verification phase, an unknown specimen is verified through the analysis of the selected regions, on the basis of a well-defined similarity measure. The experimental results, carried out on signatures from the GPDS database, demonstrate the potential of the proposed approach.