Gender Classification based on Fingerprints using SVM

Romany F. Mansour, Abdulsamad Al-Marghilnai, Meshrif Alruily

2014

Abstract

The fingerprint is commonly used biometric method for person identification. It is the most conventional and widely used technique in forensics and criminalities. Identification of the person's age and gender based on his/her fingerprint is an important step in overall person's identification. The aim of this research paper is to propose a gender classification technique based on fingerprint characteristics of individuals using discrete cosine transform (DCT). Gender classification evaluated using dimensionality reduction techniques such as Principal Component Analysis (PCA), along with Support Vector Machine (SVM). A dataset of 2600 persons of different ages and sex was collected as internal database. Of the samples tested, 1250 samples of 1375 exactly identified male samples and 1085 samples of 1225 exactly identified female samples.

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Paper Citation


in Harvard Style

F. Mansour R., Al-Marghilnai A. and Alruily M. (2014). Gender Classification based on Fingerprints using SVM . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 241-244. DOI: 10.5220/0004721602410244


in Bibtex Style

@conference{icaart14,
author={Romany F. Mansour and Abdulsamad Al-Marghilnai and Meshrif Alruily},
title={Gender Classification based on Fingerprints using SVM},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={241-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004721602410244},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Gender Classification based on Fingerprints using SVM
SN - 978-989-758-015-4
AU - F. Mansour R.
AU - Al-Marghilnai A.
AU - Alruily M.
PY - 2014
SP - 241
EP - 244
DO - 10.5220/0004721602410244