Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Online Signature Verification Method using General Handwriting Data and 1-class SVM

일반 필기 데이터와 단일 클래스 SVM을 이용한 온라인 서명 검증 기법

  • Choi, Hun (School of Electrical, Electronic & Communication Engineering, Dong-eui University) ;
  • Heo, Gyeongyong (School of Electrical, Electronic & Communication Engineering, Dong-eui University)
  • Received : 2018.07.18
  • Accepted : 2018.08.07
  • Published : 2018.11.30
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Abstract

Online signature verification is one of the simple and efficient methods of identity verification and has less resistance than other biometric technologies. To handle signature verification as a classification problem, it is necessary to gather forgery signatures, which is not easy in most practical applications. It is not easy to obtain a large number of genuine signatures either. In this paper, one class SVM is used to tackle the forgery signature problem and someone else's signatures are used as general handwriting data to solve the genuine signature problem. Someone else's signature does not share shape-based features with the signature to be verified, but it contains the general characteristics of a signature and useful in verification. Verification rate can be improved by using the general handwriting data, which can be confirmed through the experimental results.

온라인 서명 검증은 간단하면서도 효율적인 본인 확인 방법의 하나로 생체 인식에 따른 거부감이 적은 장점으로 본인 확인 용도로 사용되고 있다. 서명 검증을 분류 문제로 접근하기 위해서는 모조서명이 필요하지만, 대부분의 실용적인 응용에서 모조서명을 확보하기는 쉽지 않으며 진서명 역시 많은 양을 확보하기는 쉽지 않다. 이 논문에서는 모조서명의 확보가 어려운 문제를 해결하기 위해 단일 클래스 SVM을 사용하고, 진서명의 양이 제한되는 문제는 다른 사람의 서명 데이터를 일반 필기 데이터로 사용하여 해결하는 방법을 제시한다. 다른 사람의 서명 데이터는 검증하고자 하는 서명과 형태적인 유사점을 찾을 수 없지만, 서명에서의 일반적인 특징을 반영하고 있으므로 적은 수의 진서명만을 확보할 수 있는 경우에 사용하면 검증률을 높일 수 있으며 이는 실험 결과를 통해서 확인할 수 있다.

Keywords

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Fig. 1 Error rate with respect to threshold

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Fig. 2 Error rate with respect to the number of training samples

Table. 1 Feature vector description

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Table. 2 Error rate with respect to a verification method

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Table. 3 Error rate and decrease in error rate with respect to the number of training samples

HOJBC0_2018_v22n11_1435_t0003.png 이미지

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