Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

The effective use of the DSmT for multi-class classification

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

Offline signature verification using the discrete Radon transform and a hidden Markov model

CITATION: Coetzer, J., Herbst, B. M. & Du Preez, J. A. 2004. Offline signature verification using the discrete Radon transform and a hidden Markov model. EURASIP Journal on Applied Signal Processing, 4:559–571, doi:10.1155/S1110865704309042.The original publication is available at https://asp-eurasipjournals.springeropen.comWe developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.https://asp-eurasipjournals.springeropen.com/articles/10.1155/S1110865704309042Publisher's versio

Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model

We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features

Modelos de preclasificación biométrica

El objetivo del proyecto es estudiar alternativas para disminuir los “elevados tiempos de cálculo” necesarios en cada identificación. Este proyecto se encargará de establecer una metodología de preclasificación de los modelos existentes. Esta metodología pretende ser de ámbito general, aplicable para cualquier tipo de tecnología biométrica sin atender a la semántica de las características extraídas de la muestra biométrica. Este documento se estructura de la siguiente manera: En primera lugar se describen las motivaciones y objetivos del proyecto. Se continúa con un acercamiento al mundo biométrico y al reconocimiento de voz. Se prosigue con una revisión sobre el estado del arte del problema “elevados tiempos de cómputo”. En el siguiente capítulo se expone la metodología de trabajo desarrollada para conseguir los objetivos. En los tres capítulos siguientes se describirán la elección de la base de datos, la línea base y se concluirá con un análisis, evaluación y evolución de la solución adoptada. Se concluye la memoria con un apartado de conclusiones y futuras vías de investigación. Las últimas secciones del documento incluirán los diferentes anexos elaborados durante la creación del proyecto.Ingeniería Técnica en Sonido e Image

Offline signature verification using writer-dependent ensembles and static classifier selection with handcraft features

Orientador: Eduardo TodtDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 17/02/2022Inclui referências: p. 85-94Área de concentração: Ciência da ComputaçãoResumo: Reconhecimento e identificação de assinaturas em documentos e manuscritos são tarefas desafiadoras que ao longo do tempo vêm sendo estudadas, em especial na questão de discernir assinaturas genuínas de falsificações. Com o recente avanço das tecnologias, principalmente no campo da computação, pesquisas nesta área têm se tornado cada vez mais frequentes, possibilitando o uso de novos métodos de análise das assinaturas, aumentando a precisão e a confiança na verificação delas. Ainda há muito o que se explorar em pesquisas desta área dentro da computação. Verificações de assinaturas consistem, de forma geral, em obter características acerca de um a assinatura e utilizá-las para discerni-la das demais. Estudos propondo variados tipos de métodos foram realizados nos últimos anos a fim de aprimorar os resultados obtidos por sistemas de verificação e identificação de assinaturas. Diferentes formas de extrair características têm sido exploradas, com o o uso de redes neurais artificiais voltadas especificam ente para verificação de assinaturas, como a ResNet e a SigNet, representando o estado-da-arte nesta área de pesquisa. Apesar disso, métodos mais simples de extração de características ainda são muito utilizados, como o histograma de gradientes orientados (HOG), o Local Binary Patterns (LBP) e Local Phase Quantization (LPQ) por exemplo, apresentando, em muitos casos, resultados similares ao estado-da-arte. Não apenas isso, mas diferentes formas de combinar informações de extratores de características e resultados de classificadores têm sido propostos, como é o caso dos seletores de características, métodos de comitê de máquinas e algoritmos de análise da qualidade das características. D esta form a, o trabalho realizado consiste em explorar diferentes métodos de extração de características com binados em um conjunto de classificadores, de maneira que cada conjunto seja construído de forma dependente do autor e seja especificam ente adaptado para reconhecer as melhores características para cada autor, aprendendo quais com binações de classificadores com determinado grupo de características melhor se adaptam para reconhecer suas assinaturas. O desempenho e a funcionalidade do sistema foram comparados com os principais trabalhos da área desenvolvidos nos últimos anos, tendo sido realizados testes com as databases CEDAR, M CYT e UTSig. A pesar de não superar o estado-da-arte, o sistema apresentou bom desempenho, podendo ser com parado com alguns outros trabalhos importantes na área. Além disso, o sistema mostrou a eficiência dos classificadores Support Vector M achine(SVM ) e votadores para a realização da meta-classificação, bem como o potencial de alguns extratores de características para a área de verificação de assinaturas, com o foi o caso do Compound Local Binary Pattern(CLBP).Abstract: Signature recognition and identification in documents and manuscripts are challenging tasks that have been studied over time, especially in the matter of discerning genuine signatures from forgeries. With the recent advancement of technologies, especially in the field of computing, research in this area has become increasingly frequent, enabling the use of new methods of analysis of signatures, increasing accuracy and confidence in their verification. There is still much to be explored in research in this area within computing. Signature verification generally consists in obtaining features about a signature and using them to distinguish it from others. Studies proposing different types o f methods have been carried out in recent years in order to improve the results obtained by signature verification and identification systems. Different ways of extracting features have been explored, such as the use of artificial neural networks specifically aimed at verifying signatures, like ResNet and SigNet, representing the state-of-the-art in this research area. Despite this, simpler methods of feature extraction are still widely used, such as the Histogram of Oriented Gradients (HOG), the Local Binary Patterns (LBP) and the Local Phase Quantization (LPQ) for example, presenting, in many cases, similar results to the state-of-the-art. Not only that, but different ways of combining information from feature extractors and results from classifiers have been proposed, such as feature selectors, machine committee methods and feature quality analysis algorithms. In this way, the developed work consists in exploring different methods of features extractors combined in an ensemble, so that each ensemble is built in a writer-dependent way and is specifically adapted to recognize the best features for each author, learning which combinations of classifiers with a certain group of characteristics is better adapted to recognize their signatures. The performance and functionality of the system were compared w ith the m ain works in the area developed in recent years, w ith tests having been carried out with the CEDAR, M CYT and UTSig databases. Despite not overcoming the state-of-the-art, the system presented good performance, being able to be compared with some other important works in the area. In addition, the system showed the efficiency of Support Vector Machine(SVM ) classifiers and voters to perform the meta-classification, as well as the potential of some feature extractors for the signature verification area, such as the Compound Local Binary Pattern(CLBP)

Multi-feature approach for writer-independent offline signature verification

Some of the fundamental problems facing handwritten signature verification are the large number of users, the large number of features, the limited number of reference signatures for training, the high intra-personal variability of the signatures and the unavailability of forgeries as counterexamples. This research first presents a survey of offline signature verification techniques, focusing on the feature extraction and verification strategies. The goal is to present the most important advances, as well as the current challenges in this field. Of particular interest are the techniques that allow for designing a signature verification system based on a limited amount of data. Next is presented a novel offline signature verification system based on multiple feature extraction techniques, dichotomy transformation and boosting feature selection. Using multiple feature extraction techniques increases the diversity of information extracted from the signature, thereby producing features that mitigate intra-personal variability, while dichotomy transformation ensures writer-independent classification, thus relieving the verification system from the burden of a large number of users. Finally, using boosting feature selection allows for a low cost writer-independent verification system that selects features while learning. As such, the proposed system provides a practical framework to explore and learn from problems with numerous potential features. Comparison of simulation results from systems found in literature confirms the viability of the proposed system, even when only a single reference signature is available. The proposed system provides an efficient solution to a wide range problems (eg. biometric authentication) with limited training samples, new training samples emerging during operations, numerous classes, and few or no counterexamples