# Distance weighted discrimination of face images for gender classification

## TL;DR

This paper demonstrates the effectiveness of distance weighted discrimination for gender classification in high-dimensional face image data, outperforming traditional methods and providing insights into human gender discrimination.

## Contribution

It introduces the application of distance weighted discrimination to HDLSS face data and compares it with existing classifiers, offering new understanding of gender discrimination drivers.

## Key findings

- Distance weighted discrimination performs well in HDLSS face classification.
- Comparison shows advantages over Fisher's linear discriminant, SVM, and PCA.
- Provides visual and error analysis insights into classifier behavior.

## Abstract

We illustrate the advantages of distance weighted discrimination for classification and feature extraction in a High Dimension Low Sample Size (HDLSS) situation. The HDLSS context is a gender classification problem of face images in which the dimension of the data is several orders of magnitude larger than the sample size. We compare distance weighted discrimination with Fisher's linear discriminant, support vector machines, and principal component analysis by exploring their classification interpretation through insightful visuanimations and by examining the classifiers' discriminant errors. This analysis enables us to make new contributions to the understanding of the drivers of human discrimination between males and females.