| Autor: |
Anushikha Singh, Malay Kishore Dutta, Carlos M. Travieso, Ingrid Bonninger, Tobias Kutzner |
| Rok vydání: |
2016 |
| Předmět: |
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| Zdroj: |
2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS). |
| DOI: |
10.1109/ccintels.2016.7878206 |
| Popis: |
Real handwriting authentication systems need a robust writer identification over a long time period. The paper analyzes signature sessions of the ATV-Signature Long Term Database (ATV-SLT DB). The database contains 6 sessions generated by 27 users over 15 month. The quality change of the verification results over a period of 15 month is examined. 64static and dynamic biometric features from the ATV-SLT DB sessions are extracted and 3 different classifiers are used. For the impostor test a 7th session is added, the impostor session, with 6 signatures for each user. The best result of 99.17% success rate for a correct classification is reached with the k-Nearest Neighbor classifier. The best result of 2.47% false accepted rate is reached with Naive Bayes classifier. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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