1,915 research outputs found

    Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

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    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

    Classification of Arabic Autograph as Genuine ‎And Forged through a Combination of New ‎Attribute Extraction Techniques

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    تقترح هذه الدراسة إطارا جديدا لتقنية التحقق من التوقيع العربي. وهو يستخلص بعض السمات الديناميكية للتمييز بين التوقيعات المزورة والحقيقية. لهذا الغرض، يستخدم هذا الإطار التكيف وضعية النافذة لاستخراج تفرد من الموقعين في التوقيع بخط اليد والخصائص المحددة من الموقعين. وبناء على هذا الإطار، تقسم التوقيعات العربية أولا إلى نوافذ 14 × 14؛ كل جزء واسع بما فيه الكفاية لإدخال معلومات وافية عن أنماط الموقعين وصغيرة بما فيه الكفاية للسماح بالمعالجة السريعة. ثم، تم اقتراح نوعين من الميزات على أساس تحويل جيب التمام المنفصل، تحويل المويجة المنفصلة لاستخلاص الميزات من المنطقة ذات الاهتمام. وأخيرا، يتم اختيار شجرة القرار لتصنيف التوقيعات باستخدام الميزات المذكورة كمدخلات لها. وتجرى التقييمات على التوقيعات العربية. وكانت النتائج مشجعة جدا مع معدل تحقق 99.75٪ لاختيار سلسلة من للتوقيعات المزورة والحقيقية للتوقيعات العربية التي تفوقت بشكل ملحوظ على أحدث الأعمال في هذا المجالThis study proposes a new framework for an Arabic autograph verification technique. It extracts certain dynamic attributes to distinguish between forged and genuine signatures. For this aim, this framework uses Adaptive Window Positioning to extract the uniqueness of signers in handwritten signatures and the specific characteristics of signers. Based on this framework, Arabic autograph are first divided into 14X14 windows; each fragment is wide enough to include sufficient information about signers’ styles and small enough to allow fast processing. Then, two types of fused attributes based on Discrete Cosine Transform and Discrete Wavelet Transform of region of interest have been proposed for attributes extraction. Finally, the Decision Tree is chosen to classify the autographs using the previous attributes as its input. The evaluations are carried out on the Arabic autograph. The results are very encouraging with verification rate 99.75% for sequential selection of forged and genuine autographs for Arabic autograph that significantly outperformed the most recent work in this fiel

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

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    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

    Graph-Based Offline Signature Verification

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    Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to signature verification rely on fixed-size representations, graphs are flexible in size and allow modeling local features as well as the global structure of the handwriting. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve top results for several benchmarks, highlighting the potential of graph-based signature verification

    The effective use of the DSmT for multi-class classification

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    International audienceThe extension of the Dezert-Smarandache theory (DSmT) for the multi-class framework has a feasible computational complexity for various applications when the number of classes is limited or reduced typically two classes. In contrast, when the number of classes is large, the DSmT generates a high computational complexity. This paper proposes to investigate the effective use of the DSmT for multi-class classification in conjunction with the Support Vector Machines using the One-Against-All (OAA) implementation, which allows offering two advantages: firstly, it allows modeling the partial ignorance by including the complementary classes in the set of focal elements during the combination process and, secondly, it allows reducing drastically the number of focal elements using a supervised model by introducing exclusive constraints when classes are naturally and mutually exclusive. To illustrate the effective use of the DSmT for multi-class classification, two SVM-OAA implementations are combined according three steps: transformation of the SVM classifier outputs into posterior probabilities using a sigmoid technique of Platt, estimation of masses directly through the proposed model and combination of masses through the Proportional Conflict Redistribution (PCR6). To prove the effective use of the proposed framework, a case study is conducted on the handwritten digit recognition. Experimental results show that it is possible to reduce efficiently both the number of focal elements and the classification error rate

    One-Class Classification: Taxonomy of Study and Review of Techniques

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    One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

    Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

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    The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered
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