Siamese Sinir Ağı One-Shot Öğrenmeyi Kullanarak İmza Doğrulama

Abstract

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, 90.11, 89.99, 92.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.

Signature verification based on proposed fast hyper deep neural network

“…Arisoy [6] proposed a writer-independent signature verification system based on one-shot learning. Siamese neural network was performed in order to recognize authentic signatures from fake ones.…”

“…Biological features include the face, fingerprint, palm, iris, and retina, while behavioral features include signature and voice. In many areas of our lives, such as banks, educational institutions, attendance monitoring systems, and official document verification, where the need for authenticity is paramount, handwritten signature verification has become an integral aspect [6]. Based on the technique of acquisition, signatures are divided into offline and online signatures.…”

A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach

“…Zhou et al [41] aimed to enhance the features extraction phase in offline signature verification by extracting two types of features (static and dynamic), through combine gray level co-occurrence matrix (GLCM) with HOG, and classify the output with support vector machine (SVM) and dynamic time warping (DTW), the study indicated that improving the features extraction phase has positively affected the classification process. Additionally, Arisoy [42] used deep learning algorithms CNN and Siamese Network to improve signature verification, the proposed method was implement on four datasets (4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260), and achieved accuracy of 93.23, 90.11, 89.99, and 92.35 respectively. In addition, Tahir and Ausat [43] used Artificial Neural Network to improve offline signature verification system, the proposed method used many features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), and achieved accuracy of 82.5.…”

Offline Handwritten Signature Identification based on Hybrid Features and Proposed Deep Model

“…In verification, a test signature is compared with a model signature to check whether the test signature is genuine or counterfeit. The intrapersonal and interpersonal variabilities in signing induce changes depending on factors such as the space available for signing, the type of pen used, and the psychophysical condition of the signer, a key challenge in both signature identification and verification [2]. Signatures can now be identified in two ways: offline and online.…”