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Automatic reaction emotion estimation in a human–human dyadic setting using Deep Neural Networks

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Abstract

While Facial Expression Recognition (FER) has become a broadly applied technology in tracking individual people’s emotions, its application in estimating emotion in a human-to-human dyadic interaction is still relatively sparse. This paper describes a study where FER is applied to a dyadic video-mediated interaction to collect facial interaction data that will be used to predict the emotions of one of the interlocutors. To realize this, we utilized the histogram of oriented gradients algorithm to detect human faces from videos by analyzing every frame. Then, we used a Deep Neural Networks (DNN) model to detect the facial expressions of two people who are having a conversation on videos. We measured the facial patterns as indicators of emotions of both interlocutors during the whole interaction. Afterward, we trained a Long Short-Term Memory (LSTM) model to estimate one person’s emotions from the video. We performed the analysis on videos of a specific psychiatrist and his patients; then, we performed the patients’ emotion estimation. This work shows how our multi-stage DNN (Mini-Xception) and LSTM models can predict the reaction emotions using the patient’s facial expressions during the interaction. We believe that the proposed method can be applied to the future generation of facial expressions of virtual characters or social robots when they interact with humans.

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Notes

  1. https://www.youtube.com/channel/UClHVl2N3jPEbkNJVx-ItQIQ.

  2. http://dlib.net/.

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Authors and Affiliations

  1. Enactive Virtuality Lab, Baltic Film, Media, and Arts School, Tallinn University, Narva mnt 25, 10120, Tallinn, Estonia

    Abdallah Hussein Sham & Pia Tikka

  2. HCI Group, School of Digital Technology, Tallinn University, Narva mnt 25, 10120, Tallinn, Estonia

    Abdallah Hussein Sham & David Lamas

  3. iCV Research Lab, Institute of Technology, University of Tartu, 50411, Tartu, Estonia

    Gholamreza Anbarjafari

  4. PwC Advisory, Helsinki, Finland

    Gholamreza Anbarjafari

  5. Yildiz Technical University, Istanbul, Turkey

    Gholamreza Anbarjafari

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  1. Abdallah Hussein Sham
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  2. Pia Tikka
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  3. David Lamas
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  4. Gholamreza Anbarjafari
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Correspondence to Gholamreza Anbarjafari.

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This work has been supported by the EU Mobilitas Pluss grant (MOBTT90) of Dr. Pia Tikka, Enactive Virtuality Lab, Tallinn University (2017–2022).

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Sham, A.H., Tikka, P., Lamas, D. et al. Automatic reaction emotion estimation in a human–human dyadic setting using Deep Neural Networks. SIViP 17, 527–534 (2023). https://doi.org/10.1007/s11760-022-02257-5

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  • DOI: https://doi.org/10.1007/s11760-022-02257-5

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