Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion
  Recognition from Physiological Signals
Antoine Dubois Carlos Lima Azevedo Sonja Haustein Bruno Miranda
Emotions play a significant role in the cognitive processes of the human
brain, such as decision making, learning and perception. The use of
physiological signals has shown to lead to more objective, reliable and
accurate emotion recognition combined with raising machine learning methods.
Supervised learning methods have dominated the attention of the research
community, but the challenge in collecting needed labels makes emotion
recognition difficult in large-scale semi- or uncontrolled experiments.
Unsupervised methods are increasingly being explored, however sub-optimal
signal feature selection and label identification challenges unsupervised
methods' accuracy and applicability. This article proposes an unsupervised deep
cluster framework for emotion recognition from physiological and psychological
data. Tests on the open benchmark data set WESAD show that deep k-means and
deep c-means distinguish the four quadrants of Russell's circumplex model of
affect with an overall accuracy of 87%. Seeding the clusters with the subject's
subjective assessments helps to circumvent the need for labels.