Classifier ensemble optimization for gender classification using Genetic Algorithm

摘要

Gender classification problem is an active area of research; recently it had attracted many researchers. This study presents an efficient gender classification technique. Weighted Majority Voting (WMV) is the most popular technique used to combine individual classifiers in an ensemble based classification. Genetic Algorithm (GA) is a global optimization technique and is being widely used by the researchers in the last four decades. In this paper the optimized combination of individual classifiers is obtained using Genetic Algorithm for the problem of gender classification. The proposed method is tested on the Stanford university medical student (SUMS) frontal facial images database. The experimental results on the SUMS face database indicate that the proposed approach achieves higher accuracy then previous methods.