Score level fusion of classifiers in off-line signature verification

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Yilmaz M. B., Yanikoglu B.

INFORMATION FUSION, vol.32, pp.109-119, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32
  • Publication Date: 2016
  • Doi Number: 10.1016/j.inffus.2016.02.003
  • Journal Name: INFORMATION FUSION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.109-119
  • Keywords: Offline signature, Score-level fusion, Histogram of oriented gradients, Local binary patterns, Scale invariant feature transform, AUTHENTICATION, IDENTIFICATION, STATE
  • Akdeniz University Affiliated: Yes

Abstract

Offline signature verification is a task that benefits from matching both the global shape and local details; as such, it is particularly suitable to a fusion approach. We present a system that uses a score-level fusion of complementary classifiers that use different local features (histogram of oriented gradients, local binary patterns and scale invariant feature transform descriptors), where each classifier uses a feature-level fusion to represent local features at coarse-to-fine levels. For classifiers, two different approaches are investigated, namely global and user-dependent classifiers. User-dependent classifiers are trained separately for each user, to learn to differentiate that user's genuine signatures from other signatures; while a single global classifier is trained with difference vectors of query and reference signatures of all users in the training set, to learn the importance of different types of dissimilarities.