Features selection for offline handwritten signature verification: State of the art

This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification

Offline signature verification using DAG-CNN

This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures

Static signature verification based on machine learning

This paper describes the results of handwritten signature recognition. A handwritten signature database of 40 people made on paper and a publicly available Bengali handwritten signature database of 100 people were used for the experiments. A handwritten signature database of 40 people was collected with 10 authentic and 10 fake signatures for each person made by other people. A Bengali handwritten signature database of 100 people was collected 24 authentic and 30 forged signatures for each person. For this experiment, 20 people were randomly selected from the Bengal Handwritten Signature Database. Four options were used to reduce the signatures to sizes: 200×120, 250×150, 300×150, and 400×200 pixels for classification. These images served as input data for the proposed network architecture. As a result of testing the proposed approach, the average accuracy of correct classification for the first base of handwritten signatures reached 90.04%. For the base of Bengal handwritten signatures 97.50%

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

Application machine learning to control students trajectory

Successful and productive development of the country's digital economy is a key factor in sustainable development, production growth in all areas of socio-economic activity, which increases the country's competitiveness, the quality of life of citizens, ensures economic growth and national sovereignty. Currently, modern vocational education is moving to a qualitatively new level in connection with the introduction of a competency-based approach, which aims to provide students with tools for both understanding and action, allowing them to perceive new socio-economic realities, as well as navigate in changing conditions learning and work. The authors of the article are offered a multi-parameter model that analyzes all the parameters of a graduate based on big data and provides estimates for the qualifications of graduates

Classificação de lavouras por aprendizagem profunda com dados de sensores remotos

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2017.A classificação em larga escala em grandes regiões de plantio é um desafio, uma vez que a classificação de lavouras sem ferramentas pode ser errada. Mesmo sendo de vital importância para políticas de planejamento de commodities para o governo, pouco é realmente investido por motivos de esforço gasto. Entretanto, nos últimos anos, começou-se a utilizar dados de sensoriamento remoto, cuja informação de assinaturas espectrais dos objetos se mostrou um importante identificador para tais classificações. Técnicas recentes de aprendizado profundo, demonstram ser importantes para classificações corretas e precisas de forma autônoma. Dentro do paradigma de aprendizagem profunda, as redes neurais artificiais, sobretudo as convolutivas tem demonstrado resultados promissores. O objetivo deste trabalho é elaborar um sistema onde a partir de dados fornecidos por sensoriamento remoto, ocorra a classificação automática de lavouras. Foi usada uma adaptação da Rede Neural Convolucional VGGNet, sendo utilizados 3152 polígonos coletados pelos autores através de dados do satélite Landsat-8. Estes polígonos estão numa estrutura 15 pixels por 15 pixels, sendo 2.206 polígonos utilizados para treinamento da rede e 946 para teste. Os polígonos foram classificados em Algodão, Arroz, Cana-de-açúcar, Laranja, Milho, Soja Uva e uma categoria Outros (Solo exposto, área urbana, floresta, pasto, matagal e água). Foi alcançado um resultado de 97.57% de acurácia, 97.7% de precisão, 96.1% de recall, 96.8% de F1 score. Os mesmos polígonos de treino e de testes foram aplicados em outros 9 classificadores de Machine Learning (Support Vector Machine - SVM, Random Forest, Regressão Logística, K Neighbors, Gradient Boosting, Gaussiano, Extra Trees, Árvore de Decisão e AdaBoost). O resultado atingido pela Rede Neural Convolucional criada neste trabalho se mostrou superior ao de outros métodos de classificação, como o classificador Extra Tree que atingiu 94.1% de F1 score e 95.84% de acurácia e o Random Forest que atingiu 91.9% de F1 score e 92.75% de acurácia. O sistema se mostrou bem sucedido e foi comprovado que Redes Neurais Convolucionais conseguem ter uma boa classificação de dados de sensoriamento remoto com assinaturas espectrais para a classificação de lavouras.The large-scale sorting in large planting areas is a challenge, since classification of crops without tools may be wrong. While it is of vital importance for government commodity planning policies, little is actually invested for reasons of expenditure effort. However, in the last few years, remote sensing data began to be used, whose information on spectral signatures of the objects proved to be an important identifier for such classifications. Recent deep learning techniques prove to be important for accurate and correct classifications in an autonomous way. Within the deep learning paradigm, the artificial neural networks, especially the convolutive ones, have shown promising results. The objective of this work is to elaborate a system where from the data provided by remote sensing, automatic classification of crops occurs. An adaptation of the VGGNet Convolutional Neural Network was used, with 3,152 polygons collected by the authors using Landsat- 8 satellite data. These polygons are in a structure 15 pixels by 15 pixels, with 2,206 polygons used for network training and 946 for testing. The polygons were classified in Cotton, Rice, Sugarcane, Orange, Corn, Soybean Grape and an Other category (exposed soil, urban area, forest, pasture, scrub and water). A result of 97.57% accuracy, 97.7% accuracy, 96.1% recall, 96.8% F1 score was achieved. The same training and test polygons were applied to other 9 Machine Learning classifiers (SVM, Random Forest, Logistic Regression, K Neighbors, Gradient Boosting, Gaussian, Extra Trees, Decision Tree, and AdaBoost). The result achieved by the Convolutional Neural Network created in this work was superior to that of other methods of classification, such as the Extra Tree classifier that reached 94.1% of F1 score and 95.84% of accuracy and Random Forest that reached 91.9% of F1 score and 92.75 % accuracy. The system proved to be successful and it has been proven that Convolutional Neural Networks can have a good classification of remote sensing data with spectral signatures for the classification of crops