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Reseach Article

A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis

by A. S. Falohun, O. D. Fenwa, F. A. Ajala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 4
Year of Publication: 2016
Authors: A. S. Falohun, O. D. Fenwa, F. A. Ajala

A. S. Falohun, O. D. Fenwa, F. A. Ajala . A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis. International Journal of Computer Applications. 136, 4 ( February 2016), 43-48. DOI=10.5120/ijca2016908474

@article{ 10.5120/ijca2016908474,
author = { A. S. Falohun, O. D. Fenwa, F. A. Ajala },
title = { A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 4 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908474 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:36:09.839352+05:30
%A A. S. Falohun
%A O. D. Fenwa
%A F. A. Ajala
%T A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 4
%P 43-48
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identify people by measuring some aspect of individual’s anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. The year 2015 election in Nigeria was greeted by some petitions including under-aged voters. The need for an age and gender detector system is a major concern for organizations at all levels where integrity of information cannot be compromised. This work developed a system that determines human age-range and gender using fingerprint analysis trained with Back Propagation Neural Network (for gender classification) and DWT+PCA (for age classification). A total of 280 fingerprint samples of people with various age and gender were collected. 140 of these samples were used for training the system’s Database; 70 males and 70 females respectively. This was done for age groups 1-10, 11-20, 21-30, 31-40, 41-50, 51-60 and 61-70 accordingly. In order to determine the gender of an individual, the Ridge Thickness Valley Thickness Ratio (RTVTR) of the person was put into consideration. Result showed 80.00 % classification accuracy for females and 72.86 % for males while 115 subjects out of 140 (82.14%) were correctly classified in age.

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Index Terms

Computer Science
Information Sciences


Authentication Histogram equalization Ridge Gender Age.