Téléphone: +41 26 429 67 34
Bureau: HEIA_D20.07
BSc HES-SO en Informatique - Haute école d'ingénierie et d'architecture de Fribourg
En cours
Rôle: Requérant(e) principal(e)
Financement: TAINA Technology
Description du projet :
2017-2020, TAINA Technology
Equipe de recherche au sein de la HES-SO: Fischer Andreas
Statut: En cours
Financement: Swisscom
2018-2021, Swisscom
Financement: Hasler Foundation
2020-2021, Hasler Foundation
2021
Keller Michael, Fischer Andreas, Wessely Dorian, Mudaffer Ashna
Working Paper, February 2021, HES-SO//FR HEIA-FR, iCoSys, PICC, INNOSQUARE, Business Upper Austria,
Lien vers la publication
2019
Maergner Paul, Pondenkandath Vinaychandran, Alberti Michele, Liwicki Marcus, Riesen Kaspar, Ingold Rolf, Fischer Andreas
Pattern Recognition Letters, 2019, vol. 125, pp. 527-533
Résumé:
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.
2018
Stauffer Michael, Riesen Kaspar, Fischer Andreas
Pattern Recognition, 2018, vol. 81 pp. 240-253
In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting.
Reza Ameri Mohammad, Stauffer Michael, Riesen Kaspar, Bui Tien D., Fischer Andreas
Pattern Recognition Letters,
Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratictime approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.
Stauffer Michael, Fischer Andreas, Riesen Kaspar
The accessibility to handwritten historical documents is often constrained by the limited feasibility of automatic full transcriptions. Keyword Spotting (KWS), that allows to retrieve arbitrary query words from documents, has been proposed as alternative. In the present paper, we make use of graphs for representing word images. The actual keyword spotting is thus based on matching a query graph with all documents graphs. However, even with relative fast approximation algorithms the shear amount of matchings might limit the practical application of this approach. For this reason we present two novel filters with linear time complexity that allow to substantially reduce the number of graph matchings actually required. In particular, these filters estimate a graph dissimilarity between a query graph and all document graphs based on their node and edge distribution in a polar coordinate system. Eventually, all graphs from the document with distributions that differ to heavily from the query’s node/edge distribution are eliminated. In an experimental evaluation on four different historical documents, we show that about 90% of the matchings can be omitted, while the KWS accuracy is not negatively affected.
Diaz Moises, Fischer Andreas, Ferrer Miguel A., Plamondon Réjean
IEEE Transactions on Cybernetics, 2018, vol. 48, no. 1, pp. 228-239
The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.
2017
Garz Angelika, Seuret Mathias, Fischer Andreas, Ingold Rolf
IEEE Transactions on Human-Machine Systems, 2017, vol. 47, no. 2, pp- 181-193
In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce a user-centered segmentation method based on document graphs and scribbling interaction. The graphs capture a sparse representation of the document's structure that can then be edited by the user with a stylus on a touch-sensitive screen. We evaluate the proposed method on a newly introduced database of historical manuscripts with complex layout and demonstrate, first, that the document graphs are already close to the desired segmentation and, second, that scribbling allows a natural and efficient interaction.
Fischer Andreas, Plamondon Réjean
IEEE Transactions on Human-Machine Systems, 207, vol. 47, no. 2, pp. 169-180
When using tablet computers, smartphones, or digital pens, human users perform movements with a stylus or their fingers that can be analyzed by the kinematic theory of rapid human movements. In this paper, we present a user-centered system for signature verification that performs such a kinematic analysis to verify the identity of the user. It is one of the first systems that is based on a direct comparison of the elementary neuromuscular strokes which are detected in the handwriting. Taking into account the number of strokes, their similarity, and their timing, the string edit distance is employed to derive a dissimilarity measure for signature verification. On several benchmark datasets, we demonstrate that this neuromuscular analysis is complementary to a well-established verification using dynamic time warping. By combining both approaches, our verifier is able to outperform current state-of-the-art results in on-line signature verification.
Fischer Andreas, Riesen Kaspar, Bunke Horst
Pattern Recognition Letters, 2017, vol. 87, no. 1, pp. 55-62
Approximation of graph edit distance in polynomial time enables us to compare large, arbitrarily labeled graphs for structural pattern recognition. In a recent approximation framework, bipartite graph matching (BP) has been proposed to reduce the problem of edit distance to a cubic-time linear sum assignment problem (LSAP) between local substructures. Following the same line of research, first attempts towards quadratic-time approximation have been made recently, including a lower bound based on Hausdorff matching (Hausdorff Edit Distance) and an upper bound based on greedy assignment (Greedy Edit Distance). In this paper, we compare the two approaches and derive a novel upper bound (BP2) which combines advantages of both. In an experimental evaluation on the IAM graph database repository, we demonstrate that the proposed quadratic-time methods perform equally well or, quite surprisingly, in some cases even better than the cubic-time method.
Andreas Fischer
Proceedings of ICFHR 2018, the 16th International Conference on Frontiers in Handwriting Recognition, 5-8 August 2018, Niagara Falls, USA
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.
Proceedings of Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, 17-19 August 2018
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.
ICPR 2018, the 24th International Conference on Pattern Recognition, 20-24 August 2018, Beijing, China
Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of errortolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with (approximate) graph edit distance benchmarks.
Jean Hennebert, Andreas Fischer
Proceedings of the 2018 International Conference on High Performance Computing & Simulation (HPCS 2018), The 16th Annual Meeting, 16-20 July 2018, Orléans, France
Expression Templates is a technique allowing to write linear algebra code in C++ the same way it would be written on paper. It is also used extensively as a performance optimization technique, especially as the Smart Expression Templates form which allows for even higher performance. It has proved to be very efficient for computation on a Central Processing Unit (CPU). However, due to its design, it is not easily implemented on a Graphics Processing Unit (GPU). In this paper, we devise a set of techniques to allow the seamless evaluation of Smart Expression Templates on the GPU. The execution is transparent for the user of the library which still uses the matrices and vector as if it was on the CPU and profits from the performance and higher multi-processing capabilities of the GPU. We also show that the GPU version is significantly faster than the CPU version, without any change to the code of the user.
Proceedings of ICPRAI 2018 - International Conference on Pattern Recognition and Artificial Intelligence, Celebrating the 30th Anniversary of CENPARMI, 14-17 May 2018 + Public Lecture on 13 May 2018, Concordia University, Montréal, Canada
The Kinematic Theory of rapid human movements and its Sigma-Lognormal model enables to model human gestures, in particular complex handwriting patterns such as words, signatures and free gestures. This paper investigates the extension of the theory and its Sigma-Lognormal model from two dimensions to three, taking into account new acquisition modalities (motion capture), multiple subjects, and unconstrained motions. Despite the increased complexity and the new acquisition modalities, we demonstrate that the Sigma-Lognormal model can be successfully generalized to describe 3D human movements. Starting from the 2D model, we replace circular with spherical motions to derive a representation of unconstrained human movements with a new 3D Sigma-Lognormal model. First experiments show a high reconstruction quality with an average signal-tonoise ratio (SNR) of 18.52 dB on the HDM05 dataset. Gesture recognition using dynamic time warping (DTW) achieves similar recognition accuracies when using original and reconstructed gestures, which confirms the high quality of the proposed model.
Proceedings of DAS 2018 : 13th IAPR International Workshop on Document Analysis Systems, 24-27 April 2018, Vienna, Austria
Scanned handwritten historical documents are often not well accessible due to the limited feasibility of automatic full transcriptions. Thus, Keyword Spotting (KWS) has been proposed as an alternative to retrieve arbitrary query words from this kind of documents. In the present paper, word images are represented by means of graphs. That is, a graph is used to represent the inherent topological characteristics of handwriting. The actual keyword spotting is then based on matching a query graph with all document graphs. In particular, we make use of a fast graph matching algorithm that considers the contextual substructure of nodes. The motivation for this inclusion of node context is to increase the overall KWS accuracy. In an experimental evaluation on four historical documents, we show that the proposed procedure clearly outperforms diverse other template-based reference systems. Moreover, our novel framework keeps up or even outperforms many state-of-the-art learning-based KWS approaches.
Réalisations
