Leveraging the Bayesian Filtering Paradigm for Vision-Based Facial Affective State Estimation

Estimating a person’s affective state from facial information is an essential capability for social interaction. Automatizing
such a capability has therefore increasingly driven multidisciplinary research for the past decades. At the heart of this issue are very
challenging signal processing and artificial intelligence problems driven by the inherent complexity of human affect. We therefore
propose a principled framework for designing automated systems capable of continuously estimating the human affective state from an
incoming stream of images. First, we model human affect as a dynamical system and define the affective state in terms of valence,
arousal and their higher-order derivatives. We then pose the affective state estimation problem as a Bayesian filtering problem and
provide a solution based on Kalman filtering (KF) for probabilistic reasoning over time, combined with multiple instance sparse
Gaussian processes (MI-SGP) for inferring affect-related measurements from image sequences. We quantitatively and qualitatively
evaluate our proposed framework on the AVEC 2012 and AVEC 2014 benchmark datasets and obtain state-of-the-art results using the
baseline features as input to our MI-SGP-KF model. We therefore believe that leveraging the Bayesian filtering paradigm can pave the
way for further enhancing the design of automated systems for affective state estimation.

Jaar van publicatie:2018

Trefwoorden:affective state estimation, Facial expressions, Hausdorff distance, Kalman filtering, multiple instance regression, probabilistic reasoning, regularized least-squares, sparse Gaussian processes, variational inference

BOF-keylabel:ja

BOF-publication weight:6

CSS-citation score:1

Auteurs:International

Authors from:Government, Higher Education

Toegankelijkheid:Open