User verification using safe handwritten passwords on smartphones
Tobias Kutzner, Fanyu Ye, Ingrid Bönninger, Carlos M. Travieso, Malay Kishore Dutta, Anushikha Singh · 2015
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.