Optimal User Weighting Fusion in DWT Domain On-line Signature Verification

The on-line signature verification method in DWT domain has been proposed. Time-varying pen-position signal of the on-line signature is decomposed into sub-band signals by using the DWT. Individual features are extracted as high frequency signals in sub-band. By using the extracted feature, verification is achieved at each sub-band and then total decision is done by combining such verification results. In this paper, we introduce a user weighting fusion into the total decision. Through verification experiments, it is confirmed that there is an optimal weight combination for each user and verifiaction rate can be improved when the optimal weight combination is applied

DWT Domain Multi-matcher On-line Signature Verification System

This paper presents a multi-matcher on-line signature verification system which fuses the verification scores in pen-position parameter and pen-movement angle parameter at decision level. Features of pen-position and pen-movement angle are extracted by the sub-band decomposition using the Discrete Wavelet Transform (DWT). In the pen-position, high frequency sub-band signals are considered as individual features to enhance the difference between a genuine signature and its forgery. On the other hand, low frequency sub-band signals are utilized as the features for suppressing the intra-class variation in the penmovement angle. Verification is achieved by the adaptive signal processing using the extracted features. Verification scores in the pen-position and the pen-movement angle are integrated by using a weighted sum rule to make total decision. Experimental results show that fusion of pen-position and pen-movement angle can improve verification performance

DWT Domain On-line Signature Verification Using Pen-movement Vector

We examine a pen-movement vector parameter to reduce the computational complexity in the on-line signature verification method based on DiscreteWavelet Transform (DWT) and adaptive signal processing. The pen-movement vector is a time-varying signal which is derived from pen-position parameters and is decomposed into sub-band signals by using the DWT. Individual features are extracted as high frequency components in sub-bands. Verification is achieved in each sub-band by using the adaptive signal processing. Total decision for verification is done by combining multiple verification results. Experimental results show that the verification rate using the pen-movement vector parameter is equivalent to that of our conventional method which utilizes the pen-position parameter although computational complexity is reduced to half of that of the conventional method

Threshold Equalization for On-Line Signature Verification

In on-line signature verification, complexity of signature shape can influence the value of the optimal threshold for individual signatures. Writer-dependent threshold selection has been proposed but it requires forgery data. It is not easy to collect such forgery data in practical applications. Therefore, some threshold equalization method using only genuine data is needed. In this letter, we propose three different threshold equalization methods based on the complexity of signature. Their effectiveness is confirmed in experiments using a multi-matcher DWT on-line signature verification system

Multi-matcher on-line signature verification system in DWT domain

This paper presents a multi-matcher on-line signature verification system which fuses the verification scores in pen-position parameter and pen-movement angle parameter at decision level. Features of pen-position and pen-movement angle are extracted by the sub-band decomposition using the Discrete Wavelet Transform (DWT). In the pen-position, high frequency sub-band signals are considered as individual features to enhance the difference between a genuine signature and its forgery. On the other hand, low frequency sub-band signals are utilized as the features for suppressing the intra-class variation in the pen-movement angle. Verification is achieved by the adaptive signal processing using the extracted features. Verification scores in the pen-position and the pen-movement angle are integrated by using a weighted sum rule to make total decision. Experimental results show that fusion of pen-position and pen-movement angle can improve verification performance