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‘Exploring Gender Prediction from Digital Handwriting’ by Meryem Erbilek, Michael Fairhurst, & Cheng Li (2016)
Published onApr 03, 2020
‘Exploring Gender Prediction from Digital Handwriting’ by Meryem Erbilek, Michael Fairhurst, & Cheng Li (2016)
Erbilek, M., Fairhurst, M., & Li, C.: Exploring Gender Prediction from Digital Handwriting. In: Proceedings of the Signal Processing and Communication Application Conference, (2016)
URL: https://ieeexplore.ieee.org/document/7495858/
Abstract
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.
Critical Annotation
Erbilek et al. explore the gender prediction capacity of digitised handwriting data (in this case, specifically handwriting data captured from a digitising tablet) based on different data content sources, classifiers and features. They analyze features extracted from digital handwriting samples according to demographic characteristics and different handwriting tasks (such as form-filling, text production, cheque completion, etc.). These tasks fall under one of two categories: (1) a fixed task where the subject is told what to write and (2) a variable task where the subject chooses what to write in response to a picture. Features identified as being commonly used in handwriting processing were extracted from the handwriting data to form a feature set consisting of dynamic and static features. After these features are normalized, gender prediction performance is evaluated by using all features with different classifiers. This study demonstrates that it is possible to use digital handwriting data to predict gender. According to the classifier and feature types use, as well as the handwriting task, different accuracy were achieved. The highest accuracy achieved was between 60% -80%.
