Multimodal Fusion of Face and Palm Using Local Color Binary Patterns and Haralick Features

Face recognizable proof has drawn in numerous scientists because of its novel benefit, for example, non-contact measure for include obtaining. Varieties in brightening, posture and appearance are significant difficulties of face acknowledgment particularly when pictures are taken as dim scale. To mitigate these difficulties partially many exploration works have been completed by considering shading pictures and they have yielded better face acknowledgment rate. A strategy for perceiving face utilizing shading nearby surface highlights is depicted. Test results show that Face ID approaches utilizing shading neighborhood surface highlights astonishingly yield preferred acknowledgment rates over Face acknowledgment approaches utilizing just shading or surface data. Especially, contrasted and grayscale surface highlights, the proposed shading neighborhood surface highlights can give great coordinating with rates to confront pictures taken under extreme varieties in enlightenment and furthermore for low goal face pictures. The other biometric framework utilizes palmprint as quality for the recognizable proof and validation of people. The principal point is to extract Haralick highlights and utilization of probabilistic neural organizations for confirmation utilizing palmprint biometric quality. PolyUdatabase tests are taken from around 200 clients every client's 2 examples are gained. This palm print biometric recognizes the phony (fake) palmprint made of POP (Plaster of paris) and separates among living and non-living dependent on the entropy highlight. Test results portray that the eleven Haralick feature values are acquired in execution stage and productive precision is accomplished.

An Overview of Multimodal Biometrics Using the Face and Ear

In the recent years, we have witnessed the rapid development of face recognition, though it is still plagued by variations such as facial expressions, pose, and occlusion. In contrast to the face, the ear has a stable 3D structure and is nearly unaffected by aging and expression changes. Both the face and ear can be captured from a distance and in a nonintrusive manner, which makes them applicable to a wider range of application domains. Together with their physiological structure and location, the ear can readily serve as supplement to the face for biometric recognition. It has been a trend to combine the face and ear to develop nonintrusive multimodal recognition for improved accuracy, robustness, and security. However, when either the face or the ear suffers from data degeneration, if the fusion rule is fixed or with inferior flexibility, a multimodal system may perform worse than the unimodal system using only the modality with better quality sample. The biometric quality-based adaptive fusion is an avenue to address this issue. In this paper, we present an overview of the literature about multimodal biometrics using the face and ear. All the approaches are classified into categories according to their fusion levels. In the end, we pay particular attention to an adaptive multimodal identification system, which adopts a general biometric quality assessment (BQA) method and dynamically integrates the face and ear via sparse representation. Apart from a refinement of the BQA and fusion weights selection, we extend the experiments for a more thorough evaluation by using more datasets and more types of image degeneration.

Production of shoots from ‘Smooth Cayenne’ pineapple crowns with nitrogen fertilization

Pineapple fruit crowns are rarely used as planting material in Brazil. However, they can be used to produce plantlet in situations of scarcity of planting material of adequate quality. The aim of this work was to evaluate the production and nutritional status of early shoots of different sizes produced from ‘Smooth Cayenne’ pineapple crowns with nitrogen fertilization after the removal of its apical meristem. The experimental design adopted was that of randomized blocks in a 4x4 factorial scheme, with four nitrogen doses (5, 10, 15 and 20 g of urea per pot), four minimum shoot lengths at harvesting (10, 15, 20 and 25 cm), with four replicates. Under full sun cultivation conditions, crowns produced 18.11 and 6.83 shoots with 10 and 25 cm in length, respectively, up to 380 days after planting and removal of the apical meristem. Pineapple shoots harvested at 180 days presented better biometric quality and can be harvested up to 380 days after crown planting. Increasing urea doses reduce P, K and Ca levels in the dry mass of shoots.

Latent Fingerprint Identification Using Deep Learning Method

Digital fingerprint is one of the most consistent modalities in up to date biometrics and hence has been broadly studied and deploy in real applications. The accuracy of one Automatic Fingerprint Identification System (AFIS) largely depends on the quality of fingerprint samples, as it has an important impact on the degradation of the matching (comparison) error rates. This thesis generally focuses on the evaluation of biometric quality metrics and Fingerprint Quality Assessment (FQA), particularly in estimating the quality of gray-level latent fingerprint images or represented by minutiae set. By making a refined review of both biometric systems and relevant evaluation techniques, this contribute by the definition of a new evaluation or validation outline for estimating the performance of biometric quality metrics. It is defined to check the quality of latent fingerprint images by statistically measured parameters. In this work, an automatic Region-Of-Interest (ROI)-based latent fingerprint quality assessment technique is proposed by using deep learning. The first stage in our model uses deep learning, namely Region Convolutional Neural Network (R-CNN) to segment a latent fingerprint. In the second stage, feature vectors computed from the segmented latent fingerprint are used as input to a multi-class perceptron that predicts the value of the fingerprint. This proposed approach eliminates the need for manual ROI and feature markup by dormant examiners. Finally, experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction

RENDIMENTO DE ROSAS CULTIVADAS EM AMBIENTE PROTEGIDO SOB DIFERENTES NÍVEIS DE IRRIGAÇÃO

RENDIMENTO DE ROSAS CULTIVADAS EM AMBIENTE PROTEGIDO SOB DIFERENTES NÍVEIS DE IRRIGAÇÃO EDUARDO CARVALHO OLIVEIRA1; JACINTO DE ASSUNÇÃO CARVALHO1; FÁTIMA CONCEIÇÃO REZENDE1; ELKA FABIANA APARECIDA ALMEIDA2; SIMONE NOVAES REIS2 E SÔNIA NAOMI MIMURA2 1Universidade Federal de Lavras, Departamento de Engenharia, Campus Universitário, Caixa Postal 3037, CEP 37200-000, Lavras, MG. e-mail: [email protected], [email protected], [email protected] de Pesquisa Agropecuária de Minas Gerais, Unidade Regional EPAMIG Sul de Minas, Av. Visconde do Rio Preto, s/n, Vila São Paulo, CEP: 36.301-360, São João Del Rei, MG. e-mail: [email protected], [email protected], [email protected] 1 RESUMO A floricultura de corte tem nas rosas a sua principal exploração no Brasil e possui vasta demanda por pesquisas voltadas à produtividade e qualidade de hastes florais associadas ao adequado manejo da irrigação. Além disso, o manejo correto da irrigação está relacionado à utilização de práticas agrícolas adequadas, as quais se justificam com a redução de insumos como água, energia de bombeamento e fertilizantes. Assim, este trabalho objetivou avaliar o efeito de diferentes potenciais de água no solo sobre o rendimento e qualidade biométrica de hastes de rosas. O experimento foi realizado em casa de vegetação localizada na EPAMIG, em São João Del Rei - MG. Foi utilizado um delineamento em blocos casualizados, envolvendo seis potenciais de água no solo (-15, -30, -45, -60, -90 e -120 kPa) com cinco repetições. Os parâmetros avaliados foram: produtividade, comprimento e diâmetro das hastes e comprimento e diâmetro do botão floral. De forma geral, o déficit hídrico de até -120 kPa não interfere nas características qualitativas das hastes florais. Maiores produtividades são observadas para irrigações mais frequêntes, com -15 e -30 kPa. Palavras-chave: floricultura, roseira, qualidade, manejo da irrigação, déficit hídrico. OLIVEIRA, E. C.; CARVALHO, J. de A.; REZENDE, F. C.; ALMEIDA, E. F. A.; REIS, S. N.; MIMURA, S. N.ROSE YIELD CULTIVATED IN PROTECTED ENVIRONMENT UNDER DIFFERENT IRRIGATION LEVELS 2 ABSTRACT Roses lead exploitation of cut flowers in Brazil and demand. The present research focused on flower stems productivity and quality associated to adequate irrigation management. Moreover, the correct management of irrigation is related to the use of appropriate agricultural practices, which are justified by reducing inputs such as water, pumping energy and fertilizers. This work had the aim of assessing the effect of water deficit stress across different soil water potentials on yield and biometric quality of stems roses. The experiment was conducted in a greenhouse located at EPAMIG, São João Del Rei, MG. A randomized block design involving six water potential on soil (-15, -30, -45, -60, -90 and -120 kPa) with five replications was used. Parameters assessed were: yield, length and diameter of the stems, length and diameter of the rose bud. In general, the water deficit of up to -120 kPa does not interfere with qualitative parameters of the stem buds. Higher yields are observed for more frequent irrigations, with -15 and -30 kPa. Keywords: floriculture, rosebush, quality, irrigation management, water deficit.