Gender classification from fused multi-fingerprint types
Ogechukwu N. Iloanusi, Ugochi C. Ejiogu
Abstract
There has been a growing need for demographic information in security applications. These range from token-based demographic information elicitation to automatic gender classification or age estimation from biometric traits. Gender has been classified from facial images, voice utterances, fingerprints, hand images and emerging biometrics in the literature using local statistical or structural feature descriptors extracted from these traits. We propose a deep learning based convolutional neural network architecture for classifying gender from fingerprints of each of the five finger types and evaluate performances across trained models. We demonstrate that performance can be improved by classifying gender from fingerprints of fused combinations amongst the five right-hand finger types. The default method has been to classify gender from the index finger. However, our results show that certain finger types classify a certain gender better than the other. Leveraging on these varying strengths of the finger types we employ a fusion scheme at the abstract level, of odd number of models, trained with these fingerprint types to improve performance. Male, female and overall classification accuracies of the best fusion model are 94.7%, 88.0% and 91.3%, thus, proffering 31.02%, 7.82% and 18.72% improvement, respectively.