• ETRI Journal
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• v.26 no.6
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• pp.641-646
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• 2004

As the use of signatures for identification purposes is pervasive in society and has a long history in business, dynamic signature verification (DSV) could be an answer to authenticating a document signed electronically and establishing the identity of that document in a dispute. DSV has the advantage in that traits of the signature can be collected on a digitizer. The research question of this paper is to understand how the individual variables vary across devices. In applied applications, this is important because if the signature variables change across the digitizers this will impact performance and the ability to use those variable. Understanding which traits are consistent across devices will aid dynamic signature algorithm designers to create more robust algorithms.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.385-388
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• 2002

In this paper, a new signature verification method is proposed. In the proposed method, on-line signature features are decomposed into multi-level signals by using the discrete wavelet transform, and then they are verified using the adaptive algorithm in time-frequency domain. Through computer simulations, the effectiveness of the proposed method is examined.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.87-95
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and il usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.451-458
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and is usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.890-895
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• 2005

Dynamic (On-line) signature verification system consists of preprocessing, feature extraction, comparison and decision process for internal processing, and registration and verification windows for the user interface. We describe an implementation and design for an advanced dynamic signature verification system. Also, we suggest the method of feature extraction, matching algorithm, efficient user interface and an objective criteria for evaluating the performance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.246-246
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• 2000

다른 생체기반 검증시스템에 비해 서명 검증 시스템에서 가장 문제점은 불안정한 특징 정보를 가진다는 것이다. 그러나, 서명은 인류역사를 통해 인간에게 가장 익숙한 방법이므로 사용자에게 거부감이 없어 수많은 연구가 진행되고 있다. 본 논문에서는 이 문제를 해결하기 위해 좀더 안정화 되어 있고 유용한 특징정보를 사용하여 서명 검증 시스뎀을 구현한다

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.98-107
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• 2017

In these days, the increase of applications that highly use network resources has revealed the limitations of the current research phase from the traffic classification for network management. Various researches have been conducted to solutions for such limitations. The representative study is automatic finding of the common pattern of traffic. However, since the study of automatic signature generation is a semi-automatic system, users should collect the traffic. Therefore, these limitations cause problems in the traffic collection step leading to untrusted accuracy of the signature verification process because it does not contain any of the generated signature. In this paper, we propose an automated traffic collection, signature management, signature generation and signature verification process to overcome the limitations of the automatic signature update system. By applying the proposed method in the campus network, actual traffic signatures maintained the completeness with no false-positive.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.143-151
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• 2001

This paper investigates the development of a neural network based system for automated signature authentication that relies on an autoregressive characterization for the segments of a signature. The primary contributions of this work are tow-fold: a) the development of the neural network architecture and the modalities of training it, b) adaptation of the dynamic time warping algorithm to fomulate a new method for enabling consistent segmentation of multiple signatures from the same writer. The performance of the signature verification system has been tested using a sizable database that includes a comprehensive set of simulated and realistic forgeries. False Acceptance and False Rejection error rates of 0.78% and 1.6% respectively were obtained in tests conducted using 1920 skilled forgeries.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.250-255
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• 2000

This paper is concerning off-line signature verification using a density function which is obtained by convolving the signature image with twelve-directional 5$\times$5 gradient masks and the weighted fuzzy mean classifier. The twelve-directional density function based on Nevatia-Babu template gradient is related to the overall shape of a signature image and thus, utilized as a feature set. The weighted fuzzy mean classifier with the reference feature vectors extracted from only genuine signature samples is evaluated for the verification of freehand forgeries. The experimental results show that the proposed system can classify a signature whether genuine or forged with more than 98% overall accuracy even without any knowledge of vaned freehand forgeries.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.464-467
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• 1999

Usually, more reference signatures result in better performance in signature verification. However, registering .many signatures may be a tedious work for users, so algorithms that use less signatures for the registration without increasing error rate is needed. In this paper, we find the features such as pen-down duration, the number of locally minimum velocity points, and the number of locally maximum curvature points. Then we find the relationship between these features and the optimal decision boundary. We apply this relationship in deciding threshold for signature verification. Experimental results show that the method using three reference signatures has almost same error rate as algorithms with many references.