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Hidden Biometrics pp 185–202Cite as

From Motion to Emotion Prediction: A Hidden Biometrics Approach

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Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

In this chapter it will be discussed the capability of using motion recognition in order to predict the human emotion. Considered as a behavioral hidden biometrics approach, a specific system has been developed for this purpose wherein, several Machine-Learning approaches are considered, such as SVM, RF, MLP and KNN for classification and SVR, RFR, MLPR and KNNR for regression. The study highlights promising results in comparison to the state of the art.

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Author information

Authors and Affiliations

  1. Soft Consulting, Paris, France

    Fawzi Rida

  2. GV Lab, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Liz Rincon Ardila, Luis Enrique Coronado & Gentiane Venture

  3. Université Paris-Est, LISSI, UPEC, 94400, Vitry sur Seine, France

    Amine Nait-ali

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  1. Fawzi Rida
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  2. Liz Rincon Ardila
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  3. Luis Enrique Coronado
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  4. Amine Nait-ali
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  5. Gentiane Venture
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Corresponding author

Correspondence to Gentiane Venture .

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  1. Université Paris-Est, LISSI, UPEC, Vitry sur Seine, France

    Amine Nait-ali

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Rida, F., Ardila, L.R., Coronado, L.E., Nait-ali, A., Venture, G. (2020). From Motion to Emotion Prediction: A Hidden Biometrics Approach. In: Nait-ali, A. (eds) Hidden Biometrics. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-0956-4_11

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  • DOI: https://doi.org/10.1007/978-981-13-0956-4_11

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