Teknik pengenalan pembuluh darah punggung tangan berbasis fitur  local binary pattern

Fransisca Pontoh; Henry V.F. Kainde; Yuri Vanli Akay

doi:10.54593/awl.v2i2.15

Teknik pengenalan pembuluh darah punggung tangan berbasis fitur local binary pattern

JURNAL WIDYA ◽

10.54593/awl.v2i2.15 ◽

2021 ◽

Vol 2 (2) ◽

pp. 198-203

Author(s):

Fransisca Pontoh ◽

Henry V.F. Kainde ◽

Yuri Vanli Akay

Keyword(s):

Input Data ◽

Local Binary Pattern ◽

Palm Print

Teknologi biometrik untuk mengidentifikasi dan mengenal karakteristik bagian tubuh manusia yang unik dan tetap sudah banyak dilakukan untuk menjadi bagian dari sistem keamanan di berbagai bidang seperti sidik jari, palm print, wajah, dan iris. Tetapi semua teknik ini masih memiliki keterbatasan. Vena punggung tangan merupakan salah satu bagian dari sistem biometrik populer yang memiliki karakteristik tekstur yang berbeda pada setiap individu yang terletak di dalam tubuh sehingga sulit untuk ditempa ataupun dipalsukan, higienis dan nyaman. Metode yang digunakan adalah experimental atau berbasis pada eksperimen yang bersifat analisis. Tahapan rancangan yang dibangun meliputi input data citra, ekstraksi fitur, pencocokan dan pengenalan citra vena punggung tangan. Data yang digunakan dalam penelitian ini berupa citra punggung tangan tangan kiri dan kanan. Pengambilan gambar dilakukan menggunakan sebuah webcam yang telah dimodifikasi sehingga menangkap citra NIR. Hasil dari penelitian ini menghasilkan metode yang diusulkan dapat melakukan ekstraksi fitur pada citra pembuluh darah punggung tangan dengan akurasi maksimal mencapai 90% dan waktu komputasi selama 36.0 detik.

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Palm print recognition using Local Binary Pattern operator and support vector machines

2010 International Conference on Signal and Image Processing ◽

10.1109/icsip.2010.5697461 ◽

2010 ◽

Cited By ~ 2

Author(s):

M. Hanmandlu ◽

Ankit Gureja ◽

Ankur Jain

Keyword(s):

Support Vector Machines ◽

Local Binary Pattern ◽

Support Vector ◽

Vector Machines ◽

Palm Print

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Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k‐nearest neighbor classifier

Microscopy Research and Technique ◽

10.1002/jemt.23989 ◽

2021 ◽

Author(s):

Amjad Rehman ◽

Majid Harouni ◽

Negar Haghani Solati Karchegani ◽

Tanzila Saba ◽

Saeed Ali Bahaj ◽

...

Keyword(s):

Local Binary Pattern ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Identity Verification ◽

Microscopic Images ◽

Nearest Neighbor Classifier ◽

Palm Print ◽

Neighbor Classifier

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Palm print recognition using competitive hand valley detection, local binary pattern and probabilistic neural network

2014 International Conference on Information Technology Systems and Innovation (ICITSI) ◽

10.1109/icitsi.2014.7048246 ◽

2014 ◽

Cited By ~ 2

Author(s):

Prasetya Aria Wibawa ◽

B W Tjokorda Agung ◽

Febryanti Sthevanie

Keyword(s):

Neural Network ◽

Local Binary Pattern ◽

Probabilistic Neural Network ◽

Palm Print

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Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2500 ◽

2019 ◽

Vol 9 (1) ◽

pp. 3807-3813

Author(s):

A. Alsubari ◽

S. A. Hannan ◽

M. Alzahrani ◽

R. J. Ramteke

Keyword(s):

Euclidean Distance ◽

Local Binary Pattern ◽

Research Work ◽

Region Of Interest ◽

Detection Algorithm ◽

Zernike Moment ◽

Identification System ◽

Histogram Of Oriented Gradient ◽

Composite Feature ◽

Palm Print

Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.

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Transmission Electron Diffraction Analysis by Computer

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100067194 ◽

1970 ◽

Vol 28 ◽

pp. 40-41

Author(s):

R.A. Ploc ◽

G.H. Keech

Keyword(s):

Electron Diffraction ◽

Input Data ◽

Reciprocal Lattice ◽

Computer Programme ◽

Transmission Electron Diffraction ◽

Usual Procedure ◽

Transmission Electron ◽

Complex Shapes ◽

Computer Input ◽

Diffraction Effects

An unambiguous analysis of transmission electron diffraction effects requires two samplings of the reciprocal lattice (RL). However, extracting definitive information from the patterns is difficult even for a general orthorhombic case. The usual procedure has been to deduce the approximate variables controlling the formation of the patterns from qualitative observations. Our present purpose is to illustrate two applications of a computer programme written for the analysis of transmission, selected area diffraction (SAD) patterns; the studies of RL spot shapes and epitaxy.When a specimen contains fine structure the RL spots become complex shapes with extensions in one or more directions. If the number and directions of these extensions can be estimated from an SAD pattern the exact spot shape can be determined by a series of refinements of the computer input data.

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Concurrent error detection in array dividers by alternating input data

IEE Proceedings E Computers and Digital Techniques ◽

10.1049/ip-e.1992.0019 ◽

1992 ◽

Vol 139 (2) ◽

pp. 123 ◽

Cited By ~ 4

Author(s):

C.-L. Wey

Keyword(s):

Error Detection ◽

Input Data ◽

Concurrent Error Detection

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Bankruptcy Model Construction and its Limitation in Input Data Quality

Journal of Business and Economics ◽

10.15341/jbe(2155-7950)/02.10.2019/003 ◽

2019 ◽

Vol 10 (2) ◽

pp. 117-125

Author(s):

Dana Kubíčková ◽

◽

Vladimír Nulíček ◽

Keyword(s):

Data Quality ◽

Normal Distribution ◽

Input Data ◽

Model Construction ◽

The Third ◽

Third Stage ◽

Discriminant Analyses ◽

One Year ◽

The University ◽

Multivariate Discriminant

The aim of the research project solved at the University of Finance and administration is to construct a new bankruptcy model. The intention is to use data of the firms that have to cease their activities due to bankruptcy. The most common method for bankruptcy model construction is multivariate discriminant analyses (MDA). It allows to derive the indicators most sensitive to the future companies’ failure as a parts of the bankruptcy model. One of the assumptions for using the MDA method and reassuring the reliable results is the normal distribution and independence of the input data. The results of verification of this assumption as the third stage of the project are presented in this article. We have revealed that this assumption is met only in a few selected indicators. Better results were achieved in the indicators in the set of prosperous companies and one year prior the failure. The selected indicators intended for the bankruptcy model construction thus cannot be considered as suitable for using the MDA method.

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