Name
Abstract | ||
|---|---|---|
Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a one dimensional optimization process that is possible when a one dimensional parametrization of the curves is known. In this paper we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The proposed approach is therefore capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/CVPRW.2006.154 | CVPR Workshops |
Field | DocType | Volume |
Data mining,Dynamic programming,Computer vision,Signature recognition,Image warping,Dynamic time warping,Parametrization,Computer science,Iterative method,Precision and recall,Artificial intelligence,Correspondence problem | Conference | 2006 |
Issue | ISSN | ISBN |
1 | 2160-7508 | 0-7695-2646-2 |
Citations | PageRank | References |
3 | 0.43 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gady Agam | 1 | 391 | 43.99 |
| Suneel Suresh | 2 | 10 | 1.30 |