Auflistung nach Autor:in "Ingold, Rolf"

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Combining graph edit distance and triplet networks for offline signature verification
(Elsevier, 2019) Maergner, Paul; Pondenkandath, Vinaychandran; Alberti, Michele; Liwicki, Marcus; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas
Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.
01A - Beitrag in wissenschaftlicher Zeitschrift
Offline signature verification by combining graph edit distance and triplet networks
(Springer, 2018) Maergner, Paul; Pondenkandath, Vinaychandran; Alberti, Michele; Liwicki, Marcus; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas; Bai, Xiao; Hancock, Edwin R.; Ho, Tin Kam; Wilson, Richard C.; Biggio, Battista; Robles-Kelly, Antonio
Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.
04B - Beitrag Konferenzschrift
Offline signature verification using structural dynamic time warping
(IEEE, 2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Ingold, Rolf; Riesen, Kaspar
In recent years, different approaches for handwriting recognition that are based on graph representations have been proposed (e.g. graph-based keyword spotting or signature verification). This trend is mostly due to the availability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data structures when compared to vectorial representations. That is, graphs are able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. However, the vast majority of the proposed approaches match the graphs from a global perspective only. In the present paper, we propose to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping. Moreover, we show that the proposed approach can be readily combined with global matchings. In an experimental evaluation, we employ the novel method in a signature verification scenario on two widely used benchmark datasets. On both datasets, we empirically confirm that the proposed approach outperforms state-of-the-art methods with respect to both accuracy and runtime.
04B - Beitrag Konferenzschrift
Offline signature verification via structural methods: graph edit distance and inkball models
(IEEE, 2018) Maergner, Paul; Howe, Nicholas; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas
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.
04B - Beitrag Konferenzschrift