Siamese Sinir Ağı One-Shot Öğrenmeyi Kullanarak İmza Doğrulama

Dijitalleşmenin hayatımızın her alanında hızlanmasıyla birlikte biyometrik doğrulama yöntemlerine olan ihtiyaç da artmaktadır. Biyometrik verilerin benzersiz olması ve biyometrik doğrulamanın e-dolandırıcılık saldırılarına karşı parola tabanlı doğrulama yöntemlerine göre daha güçlü olması tercih oranını artırmıştır. Biyometrik doğrulama türlerinden olan imza doğrulama, bankacılık sistemleri, idari ve adli uygulamalar gibi birçok alanda önemli rol oynamaktadır. Kişinin kimliğini ve imza sahteciliğini tespit etmek için çevrimiçi ve çevrimdışı olmak üzere 2 tür imza doğrulaması vardır. İmzalama sırasında çevrimiçi imza doğrulaması yapılır ve kişinin imzasına ilişkin zamansal dinamik veriler mevcuttur. Çevrimdışı doğrulama, imzalandıktan sonra görüntü taranarak uygulanır ve bu doğrulama mekansal verilerle sınırlıdır. Bu nedenle, çevrimdışı imza doğrulama süreci daha zorlu bir görev olarak kabul edilir. Bu çalışmada, Siyam Sinir Ağı kullanılarak yazardan bağımsız, One-Shot Learning tabanlı çevrimdışı imza doğrulaması yapılmıştır. Derin Evrişim Sinir Ağı'nın görüntü sınıflandırması için büyük miktarda etiketli veri gerektirmesi nedeniyle, daha az sayıda imza kullanarak başarılı bir sınıflandırma yapabilen One-Shot Learning yöntemi kullanılarak gerçek ve sahte imza ayrımı sağlanmıştır. Siyam mimarisi kullanılarak imza veri setleri üzerinde yapılan deneyler sonucunda, sırasıyla 4NSigComp2012, SigComp2011, 4NSigComp2010 ve BHsig260'da 93.23, 92.11, 89.78, 91.35 doğrulama yüzdesi doğruluğu elde etmiştir.

Anahtar Kelimeler:

Çevrimdışı İmza Doğrulama, Siamese Sinir Ağı, One-shot Öğrenme, Makine Öğrenmesi, Derin Öğrenme

SIGNATURE VERIFICATION USING SIAMESE NEURAL NETWORK ONE-SHOT LEARNING

With the acceleration of digitalization in all areas of our lives, the need for biometric verification methods is increasing. The fact that biometric data is unique and biometric verification is stronger against phishing attacks compared to password-based authentication methods, has increased its preference rate. Signature verification, which is one of the biometric verification types, plays an important role in many areas such as banking systems, administrative and judicial applications. There are 2 types of signature verification, online and offline, for identifying the identity of the person and detecting signature forgery. Online signature verification is carried out during signing and temporal dynamic data are available regarding the person's signature. Offline verification is applied by scanning the image after signing, and this verification is limited to spatial data. Therefore, the offline signature verification process is considered a more challenging task. In this study, offline signature verification, independent of the writer, based on One-Shot Learning, was performed using Siamese Neural Network. Due to the fact that the Deep Convolution Neural Network requires a large amount of labeled data for image classification, real and fake signature distinction has been achieved by using the One-Shot Learning method, which can perform a successful classification by using less numbers of signature images. As a result of the experiments conducted on signature datasets, using the Siamese architecture, the proposed approach achieved percentage accuracy of 93.23, 92.11, 89.78, 91.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.

Keywords:

Offline Signature Verification, Siamese Neural Network, One-Shot Learning., Deep Learning, Machine Learning,

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