Distance weighted discrimination of face images for gender classification
This work addresses gender classification in computer vision, but it appears incremental as it applies an existing method to a specific domain problem.
The authors tackled gender classification of face images in high-dimension, low-sample-size settings by comparing distance weighted discrimination with existing methods like Fisher's linear discriminant and support vector machines, finding insights into human discrimination drivers through classification errors and visualizations.
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.