Abstract / truncated to 115 words (read the full abstract)

Signature verification deals with the problem of identifying forged signatures of a user from his/her genuine signatures. The difficulty lies in identifying allowed variations in a user’s signatures, in the presence of high intra-class and low inter-class variability (the forgeries may be more similar to a user’s genuine signature, compared to his/her other genuine signatures). The problem can be seen as a non-rigid object matching where classes are very similar. In the field of biometrics, signature is considered a behavioral biometric and the problem possesses further difficulties compared to other modalities (e.g. fingerprints) due to the added issue of skilled forgeries. A novel offline (image-based) signature verification system is proposed in this thesis. In order ... toggle 7 keywords

offline signature – histogram of oriented gradients – local binary patterns – scale invariant feature transform – user-dependent/indepent classifiers – support vector machines – user-based score normalization

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