Offline signature verification via structural methods: graph edit distance and inkball models
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Publication date
2018
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04B - Conference paper
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ICFHR2018. 2018 16th International Conference on Frontiers in Handwriting Recognition. Niagara Falls, New York, USA, 5-8 August 2018. Proceedings
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Pages / Duration
163-168
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IEEE
Place of publication / Event location
New York
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Abstract
For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graphbased representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.
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Event
16th International Conference on Frontiers in Handwriting Recognition
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05.08.2018
Conference end date
08.08.2018
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978-1-5386-5875-8
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Language
English
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Yes
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Published
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Closed
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Citation
Maergner, P., Howe, N., Riesen, K., Ingold, R., & Fischer, A. (2018). Offline signature verification via structural methods: graph edit distance and inkball models. ICFHR2018. 2018 16th International Conference on Frontiers in Handwriting Recognition. Niagara Falls, New York, USA, 5-8 August 2018. Proceedings, 163–168. https://doi.org/10.1109/ICFHR-2018.2018.00037