Latent Space Representation of Emotion
Abstract
Facial expression Recognition is a growing and important field that has applications in fields such as medicine, security, education, and entertainment. While there have been encouraging approaches that have shown accurate results on a wide variety of datasets, in many cases it is still a difficult problem to explain the results. Considering this, in this paper, we propose to model flow-based latent representations of facial expressions, which allows us to further analyze the features and grants us more granular control over which features are produced for recognition. Our work is focused on posed facial expressions with a tractable density of the latent space. We investigate the behaviour of these tractable latent space features in the case of subject dependent and independent expression recognition. We employ a flow-based generative approach with minimal supervision introduced during training and observe that traditional metrics give encouraging results. When subject independent expressions are evaluated, a shift towards a stochastic nature, in the probability space, is observed. We evaluate our flow-based representation on the BU-EEG dataset showing our approach provides good separation of classes, resulting in more explainable results.