Paul Maergner

Bio: Paul Maergner is an academic researcher from University of Fribourg. The author has contributed to research in topics: Pattern recognition (psychology) & Statistical model. The author has an hindex of 5, co-authored 11 publications receiving 70 citations. Previous affiliations of Paul Maergner include Carnegie Mellon University.

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Abstract: 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.

Offline Signature Verification Via Structural Methods: Graph Edit Distance and Inkball Models

Abstract: For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graph-based 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.

A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

Abstract: Handwritten signatures are of eminent importance in many business and legal activities around the world. That is, signatures have been used as authentication and verification measure for several centuries. However, the high relevance of signatures is accompanied with a certain risk of misuse. To mitigate this risk, automatic signature verification was proposed. Given a questioned signature, signature verification systems aim to distinguish between genuine and forged signatures. In the last decades, a large number of different signature verification frameworks have been proposed. Basically, these frameworks can be divided into online and offline approaches. In the case of online signature verification, temporal information about the writing process is available, while offline signature verification is limited to spatial information only. Hence, offline signature verification is generally regarded as the more challenging task. The present chapter reviews the field of offline signature verification and presents a comprehensive overview of methods typically employed in the general process of offline signature verification.

Offline Signature Verification using Structural Dynamic Time Warping

Abstract: 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.

Quantitative Analysis of Culture Using Millions of Digitized Books

Abstract: L'article, publie dans Science, sur une des premieres utilisations analytiques de Google Books, fondee sur les n-grammes (Google Ngrams) We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of "culturomics", focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can ...

Translation training with cross-lingual multi-media support

Abstract: An improved lecture support system integrates multi-media presentation materials with spoken content so that the listener can follow with both the speech and the supporting materials that accompany the presentation to provide additional understanding. Computer-based systems and methods are disclosed for translation of a spoken presentation (e.g., a lecture, a video) along with the accompanying presentation materials. The content of the presentation materials can be used to improve presentation translation, as it extracts supportive material from the presentation materials as they relate to the speech.

A Recurrent Neural Network based deep learning model for offline signature verification and recognition system

Abstract: With the recent advancement in information technology field, the demand to develop a person authentication system through verifying their offline signatures is gradually increasing. This type of system may be used to verify various official documents through verifying the signatures of the concerned persons present in the documents. This article proposes a Recurrent Neural Network (RNN), a deep learning network, based method to verify and recognize offline signatures of different persons. Various structural and directional features have been extracted locally from each signature sample and the generated feature vectors have been studied using two different models of RNN—long-short term memory (LSTM) and bidirectional long–short term memory (BLSTM). The performance of the proposed system has been tested on six widely used public signature databases—GPDS synthetic, GPDS-300, MCYT-75, CEDAR, BHSig260 Hindi, and BHSig260 Bengali. Experiment has also been performed using Convolutional Neural Network (CNN) to have a comparison with RNN based results. Experimental results demonstrate that the proposed RNN based signature verification and recognition system is superior over CNN and also outperforms the existing state-of-the-art results in this regard.

Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM

Abstract: In the area of digital biometric systems, the handwritten signature plays a key role in the authentication of a person based on their original samples. In offline signature verification (OSV), several problems exist that are challenging for verification of authentic or forgery signature by the digital system. Correct signature verification improves the security of people, systems, and services. It is applied to uniquely identify an individual based on the motion of pen as up and down, signature speed, and shape of a loop. In this work, the multi-level features fusion and optimal features selection based automatic technique is proposed for OSV. For this purpose, twenty-two Gray Level Co-occurrences Matrix (GLCM) and eight geometric features are calculated from pre-processing signature samples. These features are fused by a new parallel approach which is based on a high-priority index feature (HPFI). A skewness-kurtosis based features selection approach is also proposed name skewness-kurtosis controlled PCA (SKcPCA) and selects the optimal features for final classification into forged and genuine signatures. MCYT, GPDS synthetic, and CEDAR datasets are utilized for validation of the proposed system and show enhancement in terms of Far and FRR as compared to existing methods.