Offline Signature Verification based on Euclidean distance using Support Vector Machine

In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value

Offline Signature Verification Scheme

Offline signature verification schemes are necessary to determine the authenticity and genuineness of a variety of things which require certification using signatures. Most offline verification schemes till date have required perfect alignment of the signature to the specified axes. However there are situations when the sample to be verified may not be aligned to the required axis. In that situation the current verification schemes could reject the signature even though it may be genuine. The suggested scheme aims to make the verification of signatures size and angle invariant. The invariance can be achieved by scaling and rotational manipulations on the target image. The shape of a person’s signature remains similar in all translational, scaled and rotational alignments of the sign. That is the number of crests, toughs and curves remains the same irrespective of the size and orientation of the image. The ratio between consecutive crests and troughs there by remain the same and hence can be used to determine the genuineness of a signature. The proposed scheme also proposes a novel way to store the information extracted from the image after processing. The ratios obtained for verification can be stored in a linear array, which required much less space as compared to the previously followed schemes. The success of the proposed scheme can be determined from the appreciable FARs and FAAs

Graph-Based Offline Signature Verification

Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to signature verification rely on fixed-size representations, graphs are flexible in size and allow modeling local features as well as the global structure of the handwriting. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve top results for several benchmarks, highlighting the potential of graph-based signature verification

Offline Signature Verification using CNN

This paper presents the convolutional neural network for feature extraction and Support vector machine for theverification of offline signatures. The cropped signatures are used to train CNN forr extracting features. The Extracted features are classified into two classes genuine or forgery using SVM. The the new signature is tested on GPDS signature data base using the trained SVM. The dabase contains signatures of 960 users and for each user there are 24 genuine signatures and 30 forgeries. The CNN network is trained with 300 users and signatures of 400 users are used for feature learning. These 400x20x25 signatures are used 90%to train and 10% to test SVM classifier

Handwritten Signature Verification using Deep Learning

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%

Offline signature verification using DAG-CNN

This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures

Offline signature verification using classifier combination of HOG and LBP features

We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent SVMs are separately trained for each subject using genuine and random forgeries. The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries in training