eprintid: 9112
rev_number: 6
eprint_status: archive
userid: 11
dir: disk0/00/00/91/12
datestamp: 2024-09-06 03:14:33
lastmod: 2024-09-06 03:14:33
status_changed: 2024-09-06 03:14:33
type: article
metadata_visibility: show
contact_email: repository@staff.ukdw.ac.id
creators_name: , Erick Kurniawan
creators_name: , Sri Suwarno
creators_id: 0502038101
title: MULTI-LEVEL POOLING MODEL FOR FINGERPRINT-BASED GENDER CLASSIFICATION
ispublished: pub
subjects: T1
divisions: fak_tein
full_text_status: public
abstract: It has been widely reported that CNN (Convolutional Neural Network) has shown satisfactory results
in classifying images. The strength of CNN lies in the type and the number of layers that construct
it. However, the most apparent drawbacks of CNN are the requirement for a large labeled dataset and
its lengthy training time. Although datasets are available, labeling that data is a significant problem.
This work mimics the CNN model but only utilizes its pooling layers. The novelty of this model is
removing convolution layers and directly processing fingerprint images using pooling layers. Three
pooling layer models, namely maximum pooling, average pooling, and minimum pooling, are used
to generate fingerprint features to classify their owner gender. These pooling layers are arranged
consecutively up to eight levels. Removing convolution layers makes the process straightforward, and
the computation is much faster. This study utilized 200 fingerprint datasets from the NIST (National
Institute of Standards and Technology), with male and female fingerprints of 100 samples each. The
extracted features were then classified using K-NN (K-Nearest Neighbors) algorithm. The proposed
method resulted in an accuracy of 61% to 71.5% or an average of 66.25%.
date: 2023
publication: MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer
volume: 22
number: 2
publisher: Universitas Bumigora, Mataram, Nusa Tenggara Barat
pagerange: 195-206
id_number: doi:10.30812/matrik.v22i2.2551
refereed: TRUE
issn: 1858-4144
official_url: https://doi.org/10.30812/matrik.v22i2.2551
funders: srisuwarno@gmail.com
citation:   Erick Kurniawan and Sri Suwarno  (2023) MULTI-LEVEL POOLING MODEL FOR FINGERPRINT-BASED GENDER CLASSIFICATION.  MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 22 (2).  pp. 195-206.  ISSN 1858-4144     
document_url: https://katalog.ukdw.ac.id/9112/1/Multi-Level%20Pooling%20Model%20for%20Fingerprint.pdf