• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1161-1170
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• 2016

In this paper, we provide an authentication technology for verifying dynamic signature made by finger on smart phone. In the proposed method, we are using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature. In addition to the basic dynamic signature characteristic information such as appearance and velocity of a signature, we use accelerometer value supported by most of the smartphone. Signed data is re-sampled to give the same length and is normalized to a constant size. We built a test set for evaluation and conducted experiment in three ways. As results of the experiment, the proposed acceleration sensor value and 1 class model shows 6.9% less EER than previous method.

• 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 Information Processing Society Conference
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• 2016.04a
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• pp.186-188
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• 2016

본 논문에서는 모바일 환경에서 동적서명인식에 관해 원본서명과 숙련된 위조서명의 구분을 검증하는 방법을 제안한다. 속도/거리 정보 실험(Data1)과 속도/거리정보와 가속도계를 추가 실험(Data2)을 원본 서명과 위조서명에 대한 테이블을 만들고, 비교하여 원본 서명의 인식률 확인한다. 제시한 방법은 각각 모바일 환경에서 10명이 20 번삑 손가락으로 테스트 하였다. 원본서명에서 딥 러닝중의 하나인 MLP를 실험한 결과 원본 서명에서 Data1은 92%, Data2는 95%의 정확도를 보였으며, 위조서명에서 Data1은 82%, Data2는 85%를 보였다. 그리고 AE에서 실험한 결과 Data1은 원본 서명에서 Data1은 95%, Data2는 97%의 정확도를 보였으며, 위조서명에서 Data1은 91.5%, Data2는 93%의 정확도가 보였다. 실험결과 위조서명에 대해서는 MLP로 위조서명을 분류하는 것보다 OAE에서 분류하는 것이 더 좋은 정확도를 보여준다.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.663-673
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• 2005

An analysis model for the dynamics information of two-dimensional time-series patterns is described. In the proposed model, two novel transforms that visualize the dynamic characteristics are proposed. The first transform, referred to as speed equalization, reproduces a time-series pattern assuming a constant linear velocity to effectively model the temporal characteristics of the signing process. The second transform, referred to as velocity transform, maps the signal onto a horizontal vs. vertical velocity plane where the variation oi the velocities over time is represented as a visible shape. With the transforms, the dynamic characteristics in the original signing process are reflected in the shape of the transformed patterns. An analysis in the context of these shapes then naturally results in an effective analysis of the dynamic characteristics. The proposed transform technique is applied to an online signature verification problem for evaluation. Experimenting on a large signature database, the performance evaluated in EER(Equal Error Rate) was improved to 1.17$\%$ compared to 1.93$\%$ of the traditional signature verification algorithm in which no transformed patterns are utilized. In the case of skilled forgery experiments, the improvement was more outstanding; it was demonstrated that the parameter set extracted from the transformed patterns was more discriminative in rejecting forgeries