Gender Effect on Face Recognition for a Large Longitudinal Database

TL;DR

This study investigates how gender influences face recognition performance using a large longitudinal database, examining various gender distributions, classifiers, and fusion techniques to improve accuracy.

Contribution

It provides a comprehensive analysis of gender effects on face recognition and proposes an effective matching framework considering multiple variables.

Findings

Gender distribution significantly impacts recognition accuracy.

Different classifiers and fusion techniques can improve performance.

Subset size influences recognition results.

Abstract

Aging or gender variation can affect the face recognition performance dramatically. While most of the face recognition studies are focused on the variation of pose, illumination and expression, it is important to consider the influence of gender effect and how to design an effective matching framework. In this paper, we address these problems on a very large longitudinal database MORPH-II which contains 55,134 face images of 13,617 individuals. First, we consider four comprehensive experiments with different combination of gender distribution and subset size, including: 1) equal gender distribution; 2) a large highly unbalanced gender distribution; 3) consider different gender combinations, such as male only, female only, or mixed gender; and 4) the effect of subset size in terms of number of individuals. Second, we consider eight nearest neighbor distance metrics and also Support Vector Machine (SVM) for classifiers and test the effect of different classifiers. Last, we consider different fusion techniques for an effective matching framework to improve the recognition performance.