Multimodal biometrics data based gender classification using machine vision

Abstract

Gender classification from biometrics data is a significant step in forensics to categorize and minimize the suspects search from the criminal records. In this paper, we present multimodal biometrics data analysis for Gender Classification using machine learning algorithms which take input as a Face, Fingerprints and Iris images. Extensive experiments were conducted using feature level and synthesis of classifiers on the SDMULA-HMT and KVK-Multimodal datasets. Experimental results presented using multimodal biometrics data fusion schemes achieves high gender classification accuracies compared to the contemporary techniques stated in the literature.