Name
Title | ||
|---|---|---|
Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition |
Abstract | ||
|---|---|---|
The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification despite its potential applications for accessing security-sensitive facilities and for processing certain legal and historical documents. This paper presents an efficient off-line human signature recognition system based on support vector machines (SVM) and compares its performance with a traditional classification technique, multi-layer perceptrons (MLP). In both cases we propose two approaches to the problem: (1) construct each feature vector using a set of global geometric and moment-based characteristics from each signature and (2) construct the feature vector using the bitmap of the corresponding signature. We also present a mechanism to capture the intrapersonal variability of each user using just one original signature. Our results empirically show that SVM, which achieves up to 71% correct recognition rate, outperforms MLP. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.engappai.2005.12.006 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Multi-layer perceptrons,Support vector machines,Off-line signature recognition | Data mining,Feature vector,Signature recognition,Off line,Multi layer,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Bitmap,Perceptron,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 6 | 0952-1976 |
Citations | PageRank | References |
44 | 1.57 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. Frias-Martinez | 1 | 90 | 3.61 |
| Angel Sanchez | 2 | 84 | 5.73 |
| J. Velez | 3 | 44 | 1.57 |