SUSIG: An On-line Handwritten Signature Database, Associated Protocols and Benchmark Results”

In this paper we describe a new online signature database which is available for use in developing or testing signature verification systems. The SUSIG database consists of two parts, collected using different pressure sensitive tablets (one with and one without LCD display). A total of 100 people contributed to each part, resulting in a database of more than 3000 genuine and 2000 skilled forgery signatures. One of the greatest problems in constructing such a database is obtaining skilled forgeries: people who donate to a database do not have the same motivation, nor the acquired skill of a true forger intent on passing as the claimed identity. In this database, skilled forgeries were collected such that forgers saw the actual signing process played-back on the monitor and had a chance of practicing. Furthermore, for a subset of the skilled forgeries (highly skilled forgeries), the animation was mapped onto the LCD screen of the tablet so that the forgers could watch, as well as trace over the signature. Forgers in this group were also informed of how close they were to the reference signatures, so that they could improve the forgery and forgeries that were visibly dissimilar were not submitted. We describe the signature acquisition process, approaches used to collect skilled forgeries, and verification protocols which should be followed while assessing performance results. We also report performance of a state of the art online signature verification algorithm using the SUSIG database and the associated protocols. The results of this system show that the highly skilled forgery set composed of traced signatures is more difficult compared to the skilled forgery set. Furthermore, single session protocols are easier than across-session protocols. The database is made available for academic purposes through http://biometrics.sabanciuniv.edu/SUSIG

SUSIG: an on-line signature database, associated protocols and benchmark results

We present a new online signature database (SUSIG). The database consists of two parts that are collected using different pressure-sensitive tablets ( one with and the other without an LCD display). A total of 100 people contributed to each part, resulting in a database of more than 3,000 genuine signatures and 2,000 skilled forgeries. The genuine signatures in the database are real signatures of the contributors. In collecting skilled forgeries, forgers were shown the signing process on the monitor and were given a chance to practice. Furthermore, for a subset of the forgeries ( highly skilled forgeries), this animation was mapped onto the LCD screen of the tablet so that the forgers could trace over the mapped signature. Forgers in this group were also informed of how close they were to the reference signature, so that they could improve their forgery quality. We describe the signature acquisition process and several verification protocols for this database. We also report the performance of a state-of-the-art signature verification system using the associated protocols. The results show that the highly skilled forgery set is significantly more difficult compared to the skilled forgery set, providing researchers with challenging forgeries. The database is available through http://icproxy.sabanciuniv.edu:215

An Enhanced Dynamic Signature Verification using the X and Y Histogram Features

Dynamic signature verification by using histogram features is a well-known signature forgery detection technique due to its high performance. However, this technique is often limited to angular histograms derived from vectors containing two adjacent points. We propose additional new features from the X and Y histograms to overcome the limitation.  Our experiments indicate that our technique produced Under Curve Area AUC values 0.80 to detect skilled forgery and 0.91 for random forgery. Our method performed best when the verification system uses 12 of the most dominant features.  This setup produced AUC values of 0.80 to detect skilled forgery and 0.93 for random forgery. These results outperformed the original technique when the X and Y histogram features are not used that produced AUC values of 0.78 to detect skilled forgery and 0.90 for random forgery

Verifikasi Tanda Tangan Menggunakan Support Vector Machine

ABSTRAKSI: Penggunaan tanda tangan saat ini banyak digunakan untuk memverifikasi keabsahan dari berbagai transaksi keuangan. Lembar cek, credit card dan berbagai dokumen lainnya menggunakan tanda tangan sebagai pengenal keabsahan seseorang. Tetapi hingga saat ini pengecekan valid tidaknya sebuah tanda tangan masih banyak ditangani secara manual. Pengenalan secara manual tersebut cukup sulit untuk membedakan berbagai tipe pemalsuan tanda tangan yaitu random forgery, simple forgery dan skilled forgery. Untuk itu diperlukan sistem yang dapat mengenali tipe pemalsuan tanda tangan tersebut. Dimana sistem yang dibangun harus menghasilkan False Acceptance Ratio dan False Rejection Ratio sekecil mungkin. Pada tugas akhir ini diimplementasikan support vector machine sebagai classifier dan filter gabor digunakan sebagai ekstraksi ciri. Selanjutnya dilakukan penelitian terhadap tingkat akurasi sistem dalam mengenali tipe pemalsuan tanda tangan random forgery, simple forgery dan skilled forgery. Selain itu juga dilakukan analisis terhadap faktor apa saja yang mempengaruhi akurasi pada metode support vector machine. Berdasarkan pengamatan yang telah dilakukan error rate yang dihasilkan menunjukan hasil yang cukup baik, yaitu 99% pada random forgery, 87.5% pada simple forgery dan 87.5% pada skilled forgery.Kata Kunci : Verifikasi, Tanda tangan, Skilled forgery, Support vector machine (SVM), Gabor FilterABSTRACT: Signatures are often used to authorise the transfer of funds of millions of people. Bank checks, credit cards and legal documents all require our signatures. But until now most of the checking process is still handled manually, and is hard to determine the type of forgery, such as random forgery, simple forgery, and skilled forgery. A robust system has to be designed to detect various types of forgeries. The system should have an acceptable trade-off between a low false acceptance rate and a low false rejection rate. In this final project, support vector machine is implemented as a classifier and Gabor filters are used as feature extraction. Then accuracy system in recognizing random forgery, simple forgery and skilled forgery is analysis. It also examine what factors affect the accuracy of support vector machines. Based on the observations, the verification error rate have achieved the good result, 99% on random forgery, 87.5% on simple forgery and about 87.5% for skilled forgery.Keyword: Verification, signature, Skilled forgery, Support vector machine (SVM), Gabor Filter

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

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit