원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
vol.2 no.1
2009.03
pp.57-70
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Signature can be seen as an individual characteristic of a person which, if modeled with precision can be used for his/her validation. An automated signature verification technique saves valuable time and money. The paper is primarily focused on skilled forgery detection. It emphasizes on the extraction of the critical regions which are more prone to mistakes and matches them following a modular graph matching approach. The technique is robust and takes care of the inevitable intra- personal variations. The results show significant improvement over other approaches for detecting skilled forgery.
목차
Abstract
1. Introduction
2. Preprocessing:
2.1. Binarization
2.2. Noise Removal
2.3. Rotation of Signatures
2.4. Thinning of Signatures
3. Critical Region based graph matching approach:
3.1 The Polyfit Function
3.2 Extraction of critical points:
3.3 Finding the corresponding critical points among the sample signatures:
3.4 Extraction of critical regions and comparing them using graph matching:
3.5 Signature verification:
4. Results
5. Discussions and Conclusions
References
1. Introduction
2. Preprocessing:
2.1. Binarization
2.2. Noise Removal
2.3. Rotation of Signatures
2.4. Thinning of Signatures
3. Critical Region based graph matching approach:
3.1 The Polyfit Function
3.2 Extraction of critical points:
3.3 Finding the corresponding critical points among the sample signatures:
3.4 Extraction of critical regions and comparing them using graph matching:
3.5 Signature verification:
4. Results
5. Discussions and Conclusions
References