원문정보
보안공학연구지원센터(IJSIA)
International Journal of Security and Its Applications
Vol.4 No.3
2010.07
pp.35-45
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.
목차
Abstract
1. Introduction
2. Preprocessing and feature extraction
2.1 Preprocessing operations
2.2 Global and local feature extraction
3. Matching score generation
3.1 Matching score generation using Euclidean distance
3.2 Matching score generation using Mahalanobis distance
3.3 Matching score generation using Gaussian Empirical Rule
4. Fusion of multiple matchers using Support Vector Machines
5. Experimental results
6. Conclusion
References
1. Introduction
2. Preprocessing and feature extraction
2.1 Preprocessing operations
2.2 Global and local feature extraction
3. Matching score generation
3.1 Matching score generation using Euclidean distance
3.2 Matching score generation using Mahalanobis distance
3.3 Matching score generation using Gaussian Empirical Rule
4. Fusion of multiple matchers using Support Vector Machines
5. Experimental results
6. Conclusion
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보