Implementação de um Algoritmo de Reconhecimento Facial Usando EIGENFACE

Propõe-se neste artigo realizar um estudo no campo da visão computacional através da implementação do algoritmo de Eigenfaces. O objetivo dessa implementação é realizar o reconhecimento facial, desde o cadastramento de imagens até o reconhecimento e treinamento das informações na base de dados. Essa base de dados será formada por imagens capturadas por uma webcam. Será utilizada uma biblioteca open source capaz de prover ferramentas de cálculos matemáticos e filtros de imagens que formam a base do algoritmo. Um estudo de caso é realizado ao final utilizando o reconhecimento facial para cadastrar e identificar um usuário

A multiple hypothesis approach to explain species richness patterns in neotropical stream-dweller fish communities.

Several hypotheses are used to explain species richness patterns. Some of them (e.g. species-area, species-energy, environment-energy, water-energy, terrestrial primary productivity, environmental spatial heterogeneity, and climatic heterogeneity) are known to explain species richness patterns of terrestrial organisms, especially when they are combined. For aquatic organisms, however, it is unclear if these hypotheses can be useful to explain for these purposes. Therefore, we used a selection model approach to assess the predictive capacity of such hypotheses, and to determine which of them (combined or not) would be the most appropriate to explain the fish species distribution in small Brazilian streams. We perform the Akaike's information criteria for models selections and the eigenvector analysis to control the special autocorrelation. The spatial structure was equal to 0.453, Moran's I, and require 11 spatial filters. All models were significant and had adjustments ranging from 0.370 to 0.416 with strong spatial component (ranging from 0.226 to 0.369) and low adjustments for environmental data (ranging from 0.001 to 0.119) We obtained two groups of hypothesis are able to explain the richness pattern (1) water-energy, temporal productivity-heterogeneity (AIC = 4498.800) and (2) water-energy, temporal productivity-heterogeneity and area (AIC = 4500.400). We conclude that the fish richness patterns in small Brazilian streams are better explained by a combination of Water-Energy + Productivity + Temporal Heterogeneity hypotheses and not by just one