Committee machines for facial-gender recognition

Abstract

This paper presents a new approach for building a committee machine (LVQCM) that is based on learning vector quantization (LVQ) neural networks. The proposed committee machine was then applied to solve the problem of facial gender recognition. Design of individual classifiers is time consuming and results in inaccurate and unstable classifiers. Settling on the right design parameters of a classifier is a non-trivial task. To avoid the abovementioned problems, a committee machine is implemented. Experimental results based on Kuwait University and Stanford University face databases indicate that the performance of the proposed committee machine (99.02%) outperforms that of the best individual classifier used in that combination (93%). Majority voting is used for combining the individual decisions of a group of LVQ weak classifiers generated and trained under different conditions. The experimental results also show that LVQCM outperforms other recently published methods such as: the K-Means, 2^nd weight, Mahalanobis, linear discriminant, local linear discriminant, closest match, and the closest diffusion match. The implemented algorithm is not restricted to LVQ neural network and could be applied to other tytpes of neural networks.