Data fusion approach to verifying handwritten signatures on bank checks

D. J. Scott; Otman A. Basir; Khaled S. Hassanein; John S. Zelek

doi:10.1117/12.339810

29 January 1999 Data fusion approach to verifying handwritten signatures on bank checks

D. J. Scott, Otman A. Basir, Khaled S. Hassanein, John S. Zelek

Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339810
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States

Abstract

Static signature verification is a well researched problem that has not been completely solved to this date. To improve on current verification performance this research uses a pooling method which fuses together decisions of selected verification algorithms. To enhance this performance further, the decision from this method is fused with the decision of a neural network classifier. This neural network classifier offers a new approach to signature verification, since it is based on recognition techniques. The advantage of this classifier is that it incorporates different information into its decision and therefore allows the fused decision to be based on more diverse information. In contrast to other methods, this classifier requires only genuine signature samples to be trained. Experimental results show that the fusion of verification algorithms can produce better performance than any of the used methods individually.

Citation Download Citation

D. J. Scott, Otman A. Basir, Khaled S. Hassanein, and John S. Zelek "Data fusion approach to verifying handwritten signatures on bank checks", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339810

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KEYWORDS

Data fusion

Neural networks

Testing and analysis

Databases

Detection and tracking algorithms

Feature extraction

Image fusion

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