PhD. Dissertation Defense: Mustafa Berkay Yılmaz
  • FENS
  • PhD. Dissertation Defense: Mustafa Berkay Yılmaz

You are here




Mustafa Berkay Yılmaz
Computer Science, PhD Dissertation, 2015


Thesis Jury

Assoc. Prof. Berrin YANIKOĞLU (Thesis Advisor), Assoc. Prof. Hakan ERDOĞAN,

Assist. Prof. Kamer KAYA, Prof. Bülent SANKUR, Assist. Prof. Devrim ÜNAY



Date & Time: January 5th, 2015 –  2 PM

Place: FENS L058

Keywords : oine signature, histogram of oriented gradients, local binary

patterns, scale invariant feature transform, user dependent/independent classifiers,

support vector machines, user based score normalization




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 oine (image-based) signature verification system is proposed in this thesis. In order to capture the signature’s stable parts and alleviate the difficulty of global matching, local features (histogram of oriented gradients, local binary patterns) are used, based on gradient information and neighboring information inside local regions. Discriminative power of extracted features is analyzed using support vector machine (SVM) classifiers and their fusion gave better results compared to state-of-the-art. Scale invariant feature transform (SIFT) matching is also used as a complementary approach. Two dierent approaches for classifier training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to dierentiate a user’s (genuine) reference signatures from other signatures. On the other hand, a single global SVM trained with dierence vectors of query and reference signatures’ features of all users in the training set, learns how to weight the importance of different types of dissimilarities. The fusion of all classifiers achieves a 6.97% equal error rate in skilled forgery tests using the public GPDS-160 signature database.

Former versions of the system have won several signature verification competitions such as first place in 4NSigComp2010 and 4NSigComp2012 (the task without disguised signatures); first place in 4NSigComp2011 for Chinese signatures category; first place in SigWiComp2013 for all categories. Obtained results are better than those reported in the literature. One of the major benefits of the proposed method is that user enrollment does not require skilled forgeries of the enrolling user, which is essential for real life applications.