Exploring gender prediction from digital handwriting
Meryem Erbilek, Michael C. Fairhurst, Cheng Jung Li · 2016
This paper introduces an empirical investigation which directly addresses and explores gender prediction capacity from digitised handwriting data from several different perspectives - such as feature type (static/dynamic) and content (fixed/variable) types - in order to provide extensive experimental evidence and analysis to guide the development of a better understanding of the opportunities for and practical consequences of gender prediction from digital handwriting data.