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- W45492460 abstract "Biometric user authentication is a recent topic in the area of computer security. This paper presents a machine learning approach to single modality user authentication. Here support vector machines (SVM) are employed to classify dy- namic handwriting samples. The general goal of SVMs is to carry out binary clas- sifications and/or to handle multiple class problems using a combination of differ- ent SVMs. Here a multi-class SVM is proposed to execute verification as well as identification of persons based on their handwriting using a given PIN and a freely chosen PIN. In the best case (trade-off for all rates) for verification using the free PIN a false acceptance rate (FAR) of 0.0083 and an attacker acceptance rate (AAR) of 0.0241 are determined while the false rejection rate (FRR) yields zero. In iden- tification mode using the free PIN, we observe a FRR of 0.0083 and an attacker identification rate (AIR) of 0.2195 at a false identification rate (FIR) level of zero in our experiments." @default.
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- W45492460 date "2009-01-01" @default.
- W45492460 modified "2023-09-27" @default.
- W45492460 title "Support Vector Machines for Dynamic Biometric Handwriting Classification." @default.
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