Online Signature Verification Algorithm Research Articles

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

SVM Based Online Signature Verification Technique Using Reference Feature Vector

A new algorithm for online signature verification has been presented in this study. The eighty features are identified and used to characterize the online signatures. The signatures are further represented in a vector form by using symbolic representation. In proposed algorithm, two Reference feature vector for both genuine and forgery signature vector are generated for training purpose by the use of random intervals. The two class SVM classifier has been implemented for the verification task. The rigorous experiments by generating nearly a total of 1,000,000 training and testing files are performed to check the performance of the system. The popular benchmark database from first signature verification competition held in 2004 is considered for experimentation. The results are shown in terms of FAR and FRR. We have obtained a minimum value of mean FAR and mean FRR as 7.0092 and 2.2633 respectively for the same dataset in comparison to previous work. After performing sixty more iterations, we have evaluated sixty such values of FAR and FRR by changing the width of the original interval up to sixty times and analyzed behavior shows that if we keep on increasing the width of the original interval, the value of mean FAR and the corresponding FRR will keep on decreasing to the minimum level.

Hill-Climbing Attacks and Robust Online Signature Verification Algorithm against Hill-Climbing Attacks

Attacks using hill-climbing methods have been reported as a vulnerability of biometric authentication systems. In this paper, we propose a robust online signature verification algorithm against such attacks. Specifically, the attack considered in this paper is a hill-climbing forged data attack. Artificial forgeries are generated offline by using the hill-climbing method, and the forgeries are input to a target system to be attacked. In this paper, we analyze the menace of hill-climbing forged data attacks using six types of hill-climbing forged data and propose a robust algorithm by incorporating the hill-climbing method into an online signature verification algorithm. Experiments to evaluate the proposed system were performed using a public online signature database. The proposed algorithm showed improved performance against this kind of attack.

Enhanced Cancelable Biometrics for Online Signature Verification

Cancelable approaches for biometric person authentication have been studied to protect enrolled biometric data, and several algorithms have been proposed. One drawback of cancelable approaches is that the performance is inferior to that of non-cancelable approaches. In this paper, we propose a scheme to improve the performance of a cancelable approach for online signature verification. Our scheme generates two cancelable dataset from one raw dataset and uses them for verification. Preliminary experiments were performed using a distance-based online signature verification algorithm. The experimental results show that our proposed scheme is promising.

Dynamic Structural Statistical Model Based Online Signature Verification

In this article, a new dynamic structural statistical model based online signature verification algorithm is proposed, in which a method for statistical modeling the signature’s characteristic points is presented. Dynamic time warping is utilized to match two signature sequences so that correspondent characteristic point pair can be extracted from the matching result. Variations of a characteristic point are described by a multi-variable statistical probability distribution. Three methods for estimating the statistical distribution parameters are investigated. With this dynamic structural statistical model, a discriminant function can be derived to judges a signature to be genuine or forgery at the criterion of minimum potential risk. The proposed method takes advantage of both structure matching and statistical analysis. Tested in two signature databases, the proposed algorithm got much better signature verification performance than other results.

A new algorithm for on-line handwriting signature verification based on evolutionary computation

The paper proposes an on-line signature verification algorithm, through which test sample and template signatures can be optimizedly matched, based on evolutionary computation (EC). Firstly, the similarity of signature curve segment is defined, and shift and scale transforms are also introduced due to the randoness of on-line signature. Secondly, this paper puts forward signature verification matching algorithm after establishment of the mathematical model. Thirdly, the concrete realization of the algorithm based on EC is discussed as well. In addition, the influence of shift and scale on the matching result is fully considered in the algorithm. Finally, a computation example is given, and the matching results between the test sample curve and the template signature curve are analyzed in detail. The preliminary experiments reveal that the type of signature verification problem can be solved by EC.