Abstract
The paper proposes a simple, low-cost signature verification approach and software that can be employed as one of the components of a more sophisticated personal identification system. The identification is based on the use of local binary pattern features of a signature image. The Support Vector Machine (SVM) model is chosen to classify the authentic signatures from their imitations.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017 - Proceedings |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 94-98 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509042524 |
| DOIs | |
| Publication status | Published - 28 Jul 2017 |
| Event | IEEE International Conference on Computational Intelligence for Measurement Systems and Applications 2017 - Annecy, France Duration: 26 Jun 2017 → 28 Jun 2017 http://2017.civemsa.ieee-ims.org/ |
Conference
| Conference | IEEE International Conference on Computational Intelligence for Measurement Systems and Applications 2017 |
|---|---|
| Abbreviated title | CIVEMSA 2017 |
| Country/Territory | France |
| City | Annecy |
| Period | 26/06/17 → 28/06/17 |
| Internet address |
Keywords
- Local Binary Pattern
- Personal Identification
- Signature Verification
- Support Vector Machine