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In this paper, we present a multimodal approach to simultaneously analyzefacial movements and several peripheral physiological signals to decodeindividualized affective experiences under positive and negative emotionalcontexts, while considering their personalized resting dynamics. We propose aperson-specific recurrence network to quantify the dynamics present in theperson's facial movements and physiological data. Facial movement isrepresented using a robust head vs. 3D face landmark localization and trackingapproach, and physiological data are processed by extracting known attributesrelated to the underlying affective experience. The dynamical coupling betweendifferent input modalities is then assessed through the extraction of severalcomplex recurrent network metrics. Inference models are then trained usingthese metrics as features to predict individual's affective experience in agiven context, after their resting dynamics are excluded from their response.We validated our approach using a multimodal dataset consists of (i) facialvideos and (ii) several peripheral physiological signals, synchronouslyrecorded from 12 participants while watching 4 emotion-eliciting video-basedstimuli. The affective experience prediction results signified that ourmultimodal fusion method improves the prediction accuracy up to 19% whencompared to the prediction using only one or a subset of the input modalities.Furthermore, we gained prediction improvement for affective experience byconsidering the effect of individualized resting dynamics.

Facial Expression and Peripheral Physiology Fusion to Decode Individualized Affective Experience