User verification using safe handwritten passwords on smartphones
| Autor: | Carlos M. Travieso, Tobias Kutzner, Ingrid Bonninger, Fanyu Ye, Anushikha Singh, Malay Kishore Dutta |
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| Rok vydání: | 2015 |
| Předmět: |
Password
Biometrics Computer science business.industry Speech recognition Feature extraction Feature selection computer.software_genre Statistical classification ComputingMethodologies_PATTERNRECOGNITION Scoring algorithm Artificial intelligence Android (operating system) business computer Classifier (UML) Natural language processing |
| Zdroj: | IC3 |
| DOI: | 10.1109/ic3.2015.7346651 |
| Popis: | This article focuses on the writer verification using safe handwritten passwords on smartphones. We extract and select 25 static and dynamic biometric features from a handwritten character password sequence on an android touch-screen device. For the writer verification we use the classification algorithms of WEKA framework. Our 32 test persons wrote generated safe passwords with a length of 8 characters. Each person wrote their password 12 times. The approach works with 384 training samples on a supervised system. The best result of 98.72% success rate for a correct classification, the proposal reached with the KStar and k- Nearest Neighbor classifier after ranking with Fisher Score feature selection. The best result of 10.42% false accepted rate is reached with KStar classifier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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