Writer-independent Off-line Signature Verification Research Articles

Writer Independent Offline Signature Verification System using Global and Local Geometric Features

The objective of this paper is to propose an offline signature verification system (OSVS) designed for writer-independent applications. The system shall differentiate between original and forged signatures. The system encompasses key stages such as pre-processing, feature extraction, and model training and testing, employing the Support Vector Machine algorithm for classification. One challenge in creating a good OSVS is to find proper signature features to be used in the training/classification phases. In this research, global and local features are utilized. This includes signature area, mean, standard deviation, perimeter, number of connected components, number of vertical and horizontal edges, number of end points, number of branch points, and number of lines. The contributions of this paper are on several aspects of the offline signature verification process. Investigation in this study include data pre-processing techniques (normalization vs. standardization), kernel selection (Poly vs. RBF), dataset distribution for training and testing (80%-20% vs. 5-fold), and variations of the C and gamma parameters (C=1, 10, 100 and gamma=1, 10, 100). Improving the recognition rate involves removing of features with little or bad effect on the recognition rate. An algorithm for model-agnostic feature importance is executed, revealing that the most crucial features in the classification process are mean, standard deviation, perimeter, number of connected components, and number of end points. Signatures are classified as either original or forgery, and the model's performance is assessed on the CEDAR dataset. Experimental results shows a 95.65% accuracy of the proposed system when utilizing standardization with the RBF kernel.

Similarity Distance Learning on SPD Manifold for Writer Independent Offline Signature Verification

Similarity Distance Learning on SPD Manifold for Writer Independent Offline Signature Verification

A Siamese Network based Writer Independent Offline Signature Verification

Signatures are frequently used for both personal authorization and verification. The signatures on numerous papers, including legal agreements and bank checks, must be verified. Offline signature verification is a type of authentication that examines the physical action of signing while measuring the characteristics of an user's handwriting. In the present paper, an offline signature verification method was developed utilising Convolutional Siamese Network of Deep Learning [4]. Convolutional Siamese networks, which are dual networks that shares parameters, can be developed to acquire a feature map. Thus, Convolutional Siamese network is employed to verify the person's signature to a input signature that is saved in the database. Research has been conducted with a dataset of signatures that includes 750 signatures from 30 different individuals. An Accuracy of 91.6% was attained by the proposed solution.

Multi-scripted Writer Independent Off-line Signature Verification using Convolutional Neural Network

Multi-scripted Writer Independent Off-line Signature Verification using Convolutional Neural Network

Writer-Independent Offline Signature Verification Using Deep Learning

Abstract: The signature of humans is an important feature in the field of biometrics. It is used as an authentication tool especially in the banking sector because all humans have a distinct signature and each signature has its features. So human signature is used to recognize a person. There has been a fair amount of work done in the field of handwritten signature verification but still, the problem is unsolved. The main intent of signature verification is to distinguish whether the signature is genuine or forged. The signature verification can be offline or online. This is a tedious task, principally in the case of offline because the dynamic information of the signature is not available. A brief survey of various offline signature authentication methods and recent advancements in the field has been represented in this paper. Keywords: Feature extraction, Neural Network, Offline Signature verification, Preprocessing, Writer-independent.

One-Class Writer-Independent Offline Signature Verification Using Feature Dissimilarity Thresholding

Usual handwritten signature verification systems address the writer-independent (WI) approach using only bi-class robust classifiers to deal with the most challenging tasks. Indeed, WI concept, the reduced size of references and one-class signature verification are still open issues in practical cases. In this paper, we propose a one-class WI system using feature dissimilarity measures thresholding for classification and a reduced number of references. The proposed system involves the use of contourlet transform-based directional code co-occurrence matrix feature generation method. The verification is achieved through a WI threshold which is automatically selected using a new signature stability criterion. The proposed WI concept is besides addressed through the mixture of different writers’ data sets in both the design and verification stages. Experimental results show the effectiveness of the proposed system in spite of the strict verification protocol using the one-class concept, a unique threshold for accepting or rejecting a questioned signature, the reduced number of writers, and the limited number of reference signatures.

A robust dissimilarity representation for writer‐independent signature modelling

In this study, the authors present a novel dissimilarity-based signature modelling framework for writer-independent off-line signature verification. The proposed framework utilises a discrete Radon transform and a dynamic time warping algorithm for writer-independent signature representation in dissimilarity space, and a writer-specific strategy for dissimilarity normalisation. A discriminative classifier, either a discriminant function or a support vector machine, is utilised for verification purposes. Both linear and non-linear decision boundaries are considered. The authors show that the novel techniques presented in this study provide an improved platform for writer-independent signature modelling. When evaluated on Dolfing's data set, a signature database that contains 1530 genuine signatures and 3000 amateur skilled forgeries, the systems presented in this study outperform all previous systems also evaluated on this data set.

Multi-feature extraction and selection in writer-independent off-line signature verification

Some of the fundamental problems faced in the design of signature verification (SV) systems include the potentially large number of input features and users, the limited number of reference signatu...

Writer-independent off-line signature verification using surroundedness feature

The paper presents a novel set of features based on surroundedness property of a signature (image in binary form) for off-line signature verification. The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature). It also provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel. So the proposed feature set is unique in the sense that it contains both shape and texture property unlike most of the earlier proposed features for off-line signature verification. Since the features are proposed based on intuitive idea of the problem, evaluation of features by various feature selection techniques has also been sought to get a compact set of features. To examine the efficacy of the proposed features, two popular classifiers namely, multilayer perceptron and support vector machine are implemented and tested on two publicly available database namely, GPDS300 corpus and CEDAR signature database.

Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers

In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Two different scenarios were considered in our experiments. In the former, we assume that only genuine signatures and random forgeries are available to guide the search. In the latter, on the other hand, we assume that simple and simulated forgeries also are available during the optimization of the ensemble. The pool of base classifiers is trained using only genuine signatures and random forgeries. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably.