Title :
Off-line signature verification using directional PDF and neural networks
Author :
Sabourin, Robert ; Drouhard, Jean-Pierre
Author_Institution :
Lab. de Modelisation Tridimensionnelle et d´´Imagerie Ecole de Technol. Superieure, Montreal, Que., Canada
fDate :
30 Aug-3 Sep 1992
Abstract :
The first stage of a complete automatic handwritten signature verification system (AHSVS) is described in this paper. Since only random forgeries are taken into account in this first stage of decision, the directional probability density function (PDF) which is related to the overall shape of the handwritten signature has been taken into account as feature vector. Experimental results show that using both directional PDFs and the completely connected feedforward neural network classifier are valuable to build the first stage of a complete AHSVS
Keywords :
character recognition; feature extraction; feedforward neural nets; probability; AHSVS; automatic handwritten signature verification system; directional PDF; directional probability density function; feature vector; feedforward neural network classifier; neural networks; random forgeries; Cameras; Feature extraction; Feedforward neural networks; Forgery; Handwriting recognition; Image sampling; Neural networks; Probability density function; Production systems; Shape;
Conference_Titel :
Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2915-0
DOI :
10.1109/ICPR.1992.201782
