eprintid: 9078
rev_number: 6
eprint_status: archive
userid: 11
dir: disk0/00/00/90/78
datestamp: 2024-09-05 04:40:26
lastmod: 2024-09-05 04:40:26
status_changed: 2024-09-05 04:40:26
type: article
metadata_visibility: show
contact_email: repository@staff.ukdw.ac.id
creators_name: , Sri Suwarno
creators_id: 52180004
title: GENDER CLASSIFICATION BASED ON FINGERPRINT USING WAVELET AND MULTILAYER PERCEPTRON
ispublished: pub
subjects: T1
divisions: fak_tein
full_text_status: public
keywords: Politeknik Ganesha Medan, Sumatra Utara, Indonesia.
abstract: Fingerprint-based gender classification is beneficial for speeding up the
fingerprint identification of criminals, accident victims, and natural disaster victims
that are difficult to be recognized based on their physical characteristics. The
biggest obstacle to digitally classifying fingerprints is the image's poor quality.
Some methods have been developed to improve image quality through various
preprocessing, such as noise removal, background segmentation, thinning, and
binarization. However, as these processes increase the classification time, some
methods have been developed to classify fingerprints without preprocessing. One
of them that has shown excellent success is CNN (Convolutional Neural Network).
The method does not require preprocessing, but the computation time is very long
and requires large amounts of training data. This study proposed a new method that
did not need any preprocessing by using wavelet decomposition combined with the
max-pooling process to generate features. Firstly, the fingerprint image was
decomposed with a Haar wavelet of 4 levels, and each level was followed by a
max-pooling process with a 22 filter. After that, the resulting feature was used as
training data for the Multilayer Perceptron (MLP) network. In this study, the
training data was a dataset from NIST (National Institute of Standart and
Technology), with 750 fingerprints consisting of male and female fingerprints,
each as many as 375. The method could produce a total accuracy of 80.1%.
date: 2023-01-01
publication: Jurnal Sinkron : Jurnal dan Penelitian Teknik Informatika
volume: 8
number: 1
publisher: Politeknik Ganesha Medan, Sumatra Utara, Indonesia.
pagerange: 139-144
id_number: doi:10.33395/sinkron.v8i1.11925
refereed: TRUE
issn: 2541-044X
official_url: https://doi.org/10.33395/sinkron.v8i1.11925
funders: srisuwarno@gmail.com
citation:   Sri Suwarno  (2023) GENDER CLASSIFICATION BASED ON FINGERPRINT USING WAVELET AND MULTILAYER PERCEPTRON.  Jurnal Sinkron : Jurnal dan Penelitian Teknik Informatika, 8 (1).  pp. 139-144.  ISSN 2541-044X     
document_url: https://katalog.ukdw.ac.id/9078/1/Gender%20Classification%20Based.pdf