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Personality Gaze Patterns Unveiled via Automatic Relevance Determination

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Software Technologies: Applications and Foundations (STAF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11176))

Abstract

Understanding human gaze behaviour in social context, as along a face-to-face interaction, remains an open research issue which is strictly related to personality traits. In the effort to bridge the gap between available data and models, typical approaches focus on the analysis of spatial and temporal preferences of gaze deployment over specific regions of the observed face, while adopting classic statistical methods. In this note we propose a different analysis perspective based on novel data-mining techniques and a probabilistic classification method that relies on Gaussian Processes exploiting Automatic Relevance Determination (ARD) kernel. Preliminary results obtained on a publicly available dataset are provided.

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Authors and Affiliations

  1. PHuSe Lab, Department of Computer Science, Università degli Studi di Milano, Milano, Italy

    Vittorio Cuculo, Alessandro D’Amelio, Raffaella Lanzarotti & Giuseppe Boccignone

Authors
  1. Vittorio Cuculo
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  2. Alessandro D’Amelio
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  3. Raffaella Lanzarotti
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  4. Giuseppe Boccignone
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Corresponding author

Correspondence to Vittorio Cuculo .

Editor information

Editors and Affiliations

  1. Innopolis University, Innopolis, Russia

    Manuel Mazzara

  2. University of Toulouse, Toulouse Cedex 9, France

    Iulian Ober

  3. Grenoble Alpes University, Montbonnot, France

    Gwen Salaün

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Cuculo, V., D’Amelio, A., Lanzarotti, R., Boccignone, G. (2018). Personality Gaze Patterns Unveiled via Automatic Relevance Determination. In: Mazzara, M., Ober, I., Salaün, G. (eds) Software Technologies: Applications and Foundations. STAF 2018. Lecture Notes in Computer Science(), vol 11176. Springer, Cham. https://doi.org/10.1007/978-3-030-04771-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-04771-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04770-2

  • Online ISBN: 978-3-030-04771-9

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