Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/116098
Título: Signature down-sampling for finger input online signature verification
Autores/as: Saleem, Mohammad
Kovari, Bence
Clasificación UNESCO: 5701 Lingüística aplicada
110501 Método Científico
Palabras clave: Online signature verification
Sampling frequency
Classification
Fecha de publicación: 2022
Conferencia: 20th Conference of the International Graphonomics Society (IGS 2021) 
Resumen: Online signatures are widely accepted and used for authentication purposes. They are acquired using special devices with different sampling frequencies. A stylus or finger can be used as an input method. This paper studied the minimum sampling frequency required to achieve the best accuracy in online signature verification systems. Three different dynamic time warping-based verifiers were applied on the finger input signatures set of the DeepSignDB database. The results show that we can achieve a highly accurate online signature verification system for finger input signatures using lower sampling frequency, reducing time and computation costs.
URI: http://hdl.handle.net/10553/116098
Fuente: The 20th Conference of the International Graphonomics Society (IGS2021). Conference proceedings for short papers not published in the LNCS – Springer
Colección:Actas de congresos
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