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Offline Signature Verification with Random and Skilled Forgery Detection Using Polar Domain Features and Multi Stage Classification-Regression Model
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- 발행기관
- 보안공학연구지원센터(IJAST) 바로가기
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- 간행물
- International Journal of Advanced Science and Technology 바로가기
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- 통권
- Vol.59 (2013.10)바로가기
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- 페이지
- pp.27-40
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A205309
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원문정보
초록
- 영어
- Offline signature verification system finds several applications in monitory transaction systems like banks. However one of the major challenges in this direction is the capability of the system to detect skilled and unskilled forgery. Many cases of bank check forgeries have been reported. Most of the offline signature verification system adopts recognition based technique where the system classifies a given signature sample as one of the samples from the database. However detection of a forgery in a given sample is challenging as the input sample looks similar to one of the samples in the database. In this paper we propose an innovative approach for offline signature verification with polar feature descriptor for signature that contains Radon Transform and Zernike Moments. Verification is performed using Multiclass Support Vector Machine. Once a signature is verified as being of a registered class, PLS Regression is applied on the sample against all samples in the database of the verified user to obtain regression score. Log Likelihood of the sample against all sample of the user is calculated using Hidden Markov Model. Authenticity of the classification is justified if the regression score and Log Likelihood distance deviation is less than 5%. Results show that the system verifies signature with an accuracy of 98% with false acceptance rate of .8%. Proposed system also detects skilled forgery with an accuracy of 71% and Random forgery with an accuracy of 76%.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Work
4. Methodology
4.1. Pre-processing
4.2. Feature Extraction
5. Results and Discussion
6. Conclusion
References
키워드
Offline Signature Verification Skilled Forgery Detection Hidden Markov Model Partial Least Mean Square Regression Support Vector Machine Curvelet Transform Radon Transform Zernike Moments저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJAST) [Science & Engineering Research Support Center, Republic of Korea(IJAST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Advanced Science and Technology
- 간기
- 월간
- pISSN
- 2005-4238
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Advanced Science and Technology Vol.59
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