Rubens Campos Machado

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Adessowiki (http://www.adessowiki.org) is a collaborative environment for development, documentation, teaching and knowledge repository of scientific computing algorithms. The system is composed of a collection of collaborative web pages in the form of a wiki. The articles of this wiki can embed programming code that will be executed on the server when the(More)
With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint liveness detection: Convolutional Networks with random weights and Local Binary Patterns. Both techniques(More)
This work consists of the study, development and implementation of a toolbox of image processing for Python language [1]. This environment will be useful in education, research and development of final applications. The toolbox will be done using the easinesses of the Adesso project [2] for development of software of scientific computation.
This work consists in the study, development and implementation of a toolbox for image processing using the Python language and the Numerical Python package. This set has " open source " distribution and is adequate for multidimen-sional mathematical processing. Python is a modern and well projected language, interpreted, " very-high-level " , object(More)
This paper presents the Adesso, a computational environment for the development of scientific software. The Adesso environment leverages the reusable software component programming model to support the development and integration of components to several scientific programming platforms. The Adesso system is based on an XML component database and a set of(More)
With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013,(More)
The Max-Tree is an efficient data structure that represents all connected components resulting from all possible image upper threshold values. Usually, most of its nodes represent irrelevant extrema, i.e. noise, or small variations of a connected component. This paper proposes the Maximal Max-Tree Simplification (MMS) filter with a normalized threshold(More)