International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 136 - Issue 4 |
Published: February 2016 |
Authors: A. S. Falohun, O. D. Fenwa, F. A. Ajala |
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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 }, year = { 2016 }, volume = { 136 }, number = { 4 }, pages = { 43-48 }, doi = { 10.5120/ijca2016908474 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2016 %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%T %J International Journal of Computer Applications %V 136 %N 4 %P 43-48 %R 10.5120/ijca2016908474 %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.