English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The system has been assembled as a prototype then were acquired and storaged 520 images from the dorsal side of the right hand of 52 di erent users, and then is accomplished an extracting of a region of interest de ned by the largest square portion of the dorsal hand. Then a pre-processing of image has been applied using three di erent methods of image equalization and smoothing for later extraction of the veins characteristics using the Curvelet Transform in "wrapping" function and application of the Local Binary Pattern algorithm (LBP) for scanning the extracted content. On the next step, an identi cation analysis has been performed using ve di erent classi cation methods. First, a probabilistic Naive Bayes classi er was used, second a classi er based on linear regression called Kernel Nearest Neighbor (K-NN) was applied, third and fourth two algorithms based on decision trees, C4.5 and Random Forest were tested, and nally an algorithm based on arti cial neural networks Multilayer Perceptron was performed. The classi ers have been tested using the cross-validation method, and the information was separated by 10 folds wherein 10% of the data were used for training and 90% of the data were used for testing. From the same data resulted of the pre-processing step, two algorithms have been applied for selection features, the rst based on the correlation based feature selection and the second in selecting attributes based to the concept of entropy data. The results proof that the equalization method by contrast limited adaptive histogram equalization, in the pre-processing stage, shown the best results. From the application of classi ers, the best result was achieved by using the arti cial neural network proposal and the false acceptance rate (FAR) and false rejection rate (FRR) found through the processing were estimated in 0.038 and 0.003 respectively. Tests were also performed to assess the minimum amount of images needed to identify people and as result ve images per user were found as the ideal number. Finally, the assessment of the biometric system permanence was performed using acquired images after a year of the rst analysis and the results shown that the system is robust, even that the pictures contain minor changes proportional to index variations of body mass of users.

Proposição de um algoritmo para identificação biométrica de pessoas baseado nos padrões de veias das mãos.