TY - GEN
T1 - Offline signature verification through probabilistic neural network
AU - Yin, Ooi Shih
AU - Jin, Andrew Teoh Beng
AU - Yan, Hiew Bee
AU - Han, Pang Ying
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - In this paper, we show the positive potential of verifying the offline handwritten signatures through discrete Radon transform (DRT), principle component analysis (PCA) and probabilistic neural network (PNN). Satisfactory results are obtained with 1.51%, 3.23%, and 13.07% equal error rate (EER) for random, casual, and skilled forgeries respectively on our independent database.
AB - In this paper, we show the positive potential of verifying the offline handwritten signatures through discrete Radon transform (DRT), principle component analysis (PCA) and probabilistic neural network (PNN). Satisfactory results are obtained with 1.51%, 3.23%, and 13.07% equal error rate (EER) for random, casual, and skilled forgeries respectively on our independent database.
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M3 - Conference contribution
AN - SCOPUS:84862080588
SN - 9788086943886
T3 - 18th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2010 - In Co-operation with EUROGRAPHICS, Full Papers Proceedings
SP - 31
EP - 38
BT - 18th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2010 - In Co-operation with EUROGRAPHICS, Full Papers Proceedings
T2 - 18th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2010
Y2 - 1 February 2010 through 4 February 2010
ER -