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<p>Shape-writing is a text entry method that allows users to type words on mobile devices by gliding their finger across the keyboard from one character to the next. This creates a trajectory of touch coordinates that contains rich information about the user. Previous work exploited this information to create Machine Learning (ML) models to predict demographic and behavioral targets, such as age, nationality, or handedness. However, previous work used pseudo-grid search, which is a bit tedious and rather inefficient. We show how to find better models with Automated Machine Learning (AutoML), by completely automating the architecture design process, outperforming all models reported in previous work. Our study suggests that researchers should incorporate AutoML to their training pipelines, as classification performance will likely be better than manually designing the model architecture. Taken together, our results show that it is possible to decode user’s latent information from shape-writing trajectories with higher performance than previously reported.</p>

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AutoML for shape-writing biometrics

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Louis Weber,
  • Luis A. Leiva

摘要

Shape-writing is a text entry method that allows users to type words on mobile devices by gliding their finger across the keyboard from one character to the next. This creates a trajectory of touch coordinates that contains rich information about the user. Previous work exploited this information to create Machine Learning (ML) models to predict demographic and behavioral targets, such as age, nationality, or handedness. However, previous work used pseudo-grid search, which is a bit tedious and rather inefficient. We show how to find better models with Automated Machine Learning (AutoML), by completely automating the architecture design process, outperforming all models reported in previous work. Our study suggests that researchers should incorporate AutoML to their training pipelines, as classification performance will likely be better than manually designing the model architecture. Taken together, our results show that it is possible to decode user’s latent information from shape-writing trajectories with higher performance than previously reported.