Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition

TL;DR

This paper presents a new fingerprint gender classification method combining wavelet transform and SVD, achieving over 88% accuracy using KNN classifier on a large fingerprint database.

Contribution

The study introduces a novel combination of DWT and SVD features for fingerprint gender classification, demonstrating high accuracy with a simple KNN classifier.

Findings

Achieved overall classification accuracy of 88.28%.

Gender classification accuracy exceeds 95% for specific fingers.

Method effective on a large fingerprint dataset of 3570 samples.

Abstract

A novel method of gender Classification from fingerprint is proposed based on discrete wavelet transform (DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computed from all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVD of fingerprint images. K nearest neighbor (KNN) used as a classifier. This method is experimented with the internal database of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints. Finger-wise gender classification is achieved which is 94.32% for the left hand little fingers of female persons and 95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested is attained as 91.67% and 84.69% for female persons respectively. Overall classification rate is 88.28% has been achieved.