Overview of fingerprint recognition system

Ali, Mouad M. H.; Mahale, Vivek; Yannawar, Pravin; Gaikwad, Asha

doi:10.1109/iceeot.2016.7754900

Cited by 91 publications

(33 citation statements)

References 30 publications

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“…They are called minutiae (features or singular points) that is unique for each fingerprint characteristics defining the points of changes in the structure of papillary lines (ending, bifurcation, break, etc. ), the orientation of papillary lines and the coordinates of these points and the characteristics of fingerprints [1]. They do not change along the life.…”

Section: Fingerprintmentioning

confidence: 96%

Survey of Primary Methods of Fingerprint Feature Extraction

Wahhab¹,

Alanssari²

2018

CTAC&R

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Proving the identity of the individual today is an urgent need in many areas, including social security, financial, criminal and other fields. Biometrics is interested in identifying individuals through personality traits, eye color, fingerprints, height, facial appearance, and signature. Image processing technology supports most of the techniques used in biometrics, by improving images and matching images, to identify the individual identity. Fingerprint recognition is some of the most well-known biometrics, and it the best the used biometric result for confirmation on computer systems. Technic fingerprinting has a great popularity for simplicity of acquisition, the use and approval when compared to other systems. Fingerprint Recognition System consists of four steps: firstly, Image acquisition (image fingerprint) through sensors .Secondly, processing the image so that it obscures the noise that exists, clarifying the hills. Thirdly, Extracting features of injury fingerprints. Fourth, compare the acquired features with features of fingerprint fingerprints in databases. The aim of this paper is to present the latest research in the applications of fingerprint recognition systems.

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Section: Fingerprintmentioning

confidence: 96%

Survey of Primary Methods of Fingerprint Feature Extraction

Wahhab¹,

Alanssari²

2018

CTAC&R

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“… da íris (Daugman, 2004;Bowyer, Hollingsworth, & Flynn, 2013): uma técnica que se baseia na extração de características da textura da íris; ela é interessante por apresentar 42 boa acurácia; necessita de equipamentos específicos para gerar imagens dos olhos e possui limitações quanto a movimentação da cabeça e da pálpebra;  da face (Parkhi, Vedaldi, & Zisserman, 2015;Jafri & Arabnia, 2009): busca identificar pessoas por diferentes características ligadas a geometria da face e outras particularidades; sua precisão é boa e tem crescido, principalmente com a evolução dos dispositivos fotográficos e das técnicas de modo geral; necessita de dispositivos para capturar imagens e é intrusiva;  da voz (Padmanabhan & Premkumar, 2015): identifica pessoas através do padrão de voz; apresenta boa acurácia em ambientes controlados; porém, pode ter problemas com ruídos sonoros e distância do microfone, além de ser intrusiva;  da digital dos dedos e mãos (Ali, Mahale, Yannawar, & Gaikwad, 2016;Maio, Maltoni, Cappelli, Wayman, & Jain, 2002): o reconhecimento de digitais é amplamente utilizado; possui boa acurácia, porém, necessita de hardware específico para capturar as digitais.…”

Section: Reconhecimento De Usuáriounclassified

Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

Cruz

Duarte

Goldschmidt

2017

RBIE

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Resumo A autenticação de usuário nos ambientes virtuais de aprendizagem (AVAs) existentes é, em geral, pontual e intrusiva, ocorrendo no momento em que o usuário Abstract The authentication of users on a Virtual Learning Environment (VLE) is, in general, punctual and intrusive, occurring when the user connects to the environment, by typing his password. Such approach allows, after the initial login, that unauthenticated users take the role of authenticated users and perform tasks in the environment, causing, among other things, distortions in the perception about the academic performance of students. The objective of this work is, thus, to propose a mechanism to execute periodic and non-intrusive authentications of users in VLEs.The proposed mechanism uses machine learning techniques to build recognition models based on the keystroke dynamics of users and it is also independent of the used VLE. A prototype of the proposed mechanism, integrated with Moodle, was implemented and applied to a postgraduate course with seventeen users. The recognition models generated by the prototype in the case study showed a performance above 92% of accuracy, which is a positive indication about the viability of the utilization of the proposed mechanism.

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“…With the development of computer and image processing techniques, there are many effective nonlinear-data processing and pattern recognition methods (Kilde and Halgren, 2014;Ali et al, 2016;Wang et al, 2016;Wong, 2017a,b, 2018;Bouguelia et al, 2018;Li et al, 2018a,b), which can be applied to image recognition. Manifold learning is a basic method in pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Combing K-means Clustering and Local Weighted Maximum Discriminant Projections for Weed Species Recognition

Zhang

Guo²,

Wang

2019

Front. Comput. Sci.

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Weed species recognition is the premise to control weeds in smart agriculture. It is a challenging topic to control weeds in field, because the weeds in field are quite various and irregular with complex background. A recognition method of weed species in crop field is proposed based on Grabcut and local discriminant projections (LWMDP) algorithm. First, Grabcut is used to remove the most background and K-means clustering (KMC) is utilized to segment weeds from the whole image. Then, LWMDP is employed to extract the low-dimensional discriminant features. Finally, the support vector machine (SVM) classifier is adopted to identify weed species. The characteristics of the proposed method are that, (1) Grabcut and KMC are combined to remove the most of background and obtain the clean weed image, which can reduce the burden of the subsequent feature extraction; (2) LWMDP can project the high-dimensional original image into the low-dimensional subspace, such that the different-class data points are mapped as far as possible, while the within-class data points are projected as close as possible, and the matrix inverse computation is ignored in the generalized eigenvalue problem, thus the small sample size (SSS) problem is avoided naturally. The experimental results on the dataset of the weed species images show that the proposed method is effective for weed identification species, and can preliminarily meet the requirements of multi-row spraying of crop based on machine vision.

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Overview of fingerprint recognition system

Cited by 91 publications

References 30 publications

Survey of Primary Methods of Fingerprint Feature Extraction

Survey of Primary Methods of Fingerprint Feature Extraction

Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

Combing K-means Clustering and Local Weighted Maximum Discriminant Projections for Weed Species Recognition

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