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

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

Fuzzy Logic Classification of Handwritten Signature Based Computer Access and File Encryption

Often times computer access and file encryption is successful based on how complex a password will be, how often users could change their complex password, the length of the complex password and how creative users are in creating a complex passsword to stand against unauthorized access to computer resources or files. This research proposes a new way of computer access and file encryption based on the fuzzy logic classification of handwritten signatures. Feature extraction of the handwritten signatures, the Fourier transformation algorithm and the k-Nearest Algorithm could be implemented to determine how close the signature is to the signature on file to grant or deny users access to computer resources and encrypted files. lternatively implementing fuzzy logic algorithms and fuzzy k-Nearest Neighbor algorithm to the captured signature could determine how close a signature is to the one on file to grant or deny access to computer resources and files. This research paper accomplishes the feature recognition firstly by extracting the features as users sign their signatures for storage, and secondly by determining the shortest distance between the signatures. On the other hand this research work accomplish the fuzzy logic recognition firstly by classifying the signature into a membership groups based on their degree of membership and secondly by determining what level of closeness the signatures are from each other. The signatures were collected from three selected input devices- the mouse, I-Pen and the IOGear. This research demonstrates which input device users found efficient and flexible to sign their respective names. The research work also demonstrates the security levels of implementing the fuzzy logic, fuzzy k-Nearest Neighbor, Fourier Transform.Master'sCollege of Arts and Sciences: Computer ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/117719/1/Kwarteng.pd

Parameterization of written signatures based on EFD

In this work we propose a method to quantify written signatures from digitalized images based on the use of Elliptical Fourier Descriptors (EFD). As usually signatures are not represented as a closed contour, and being that a necessary condition in order to apply EFD, we have developed a method that represents the signatures by means of a set of closed contours. One of the advantages of this method is that it can reconstruct the original shape from all the coefficients, or an approximated shape from a reduced set of them finding the appropriate number of EFD coefficients required for preserving the important information in each application. EFD provides accurate frequency information, thus the use of EFD opens many possibilities. The method can be extended to represent other kind of shapes

Dynamic signature verification based on hybrid wavelet-Fourier transform

In this paper, we propose a dynamic signature verification system which integrates hybrid of Discrete Wavelet Transform and Discrete Fourier Transform (DWT-DFT) for feature extraction. In feature matching, Euclidean distance and Enveloped Euclidean distance (EED) (a variant of Euclidean distance) are used. Distances of features are fused into a final score value and used to classify whether a genuine or a forgery signature. A benchmark database, SVC2004 which compose of Task 1 dataset and Task 2 dataset validate the effectiveness of this proposed system. Experimental results reveal a 7.08% EER for skilled forgeries and 2.37% EER of random forgeries in Task 1 dataset; and 8.61% EER for skilled forgeries and 2.05% EER for random forgeries in Task 2 datase

Optimized biometric system based iris-signature for human identification

This research aimed at comparing iris-signature techniques, namely the Sequential Technique (ST) and the Standard Deviation Technique (SDT). Both techniques were measured by Backpropagation (BP), Probabilistic, Radial basis function (RBF), and Euclidian distance (ED) classifiers. A biometric system-based iris is developed to identify 30 of CASIA-v1 and 10 subjects from the Real-iris datasets. Then, the proposed unimodal system uses Fourier descriptors to extract the iris features and represent them as an iris-signature graph. The 150 values of input machine vector were optimized to include only high-frequency coefficients of the iris-signature, then the two optimization techniques are applied and compared. The first optimization (ST) selects sequentially new feature values with different lengths from the enrichment graph region that has rapid frequency changes. The second technique (SDT) chooses the high variance coefficients as a new feature of vectors based on the standard deviation formula. The results show that SDT achieved better recognition performance with the lowest vector-lengths, while Probabilistic and BP have the best accuracy

Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio