Automatic Recognition of Facial Displays of Unfelt Emotions

TL;DR

This paper introduces SASE-FE, a new dataset and method for distinguishing genuine from unfelt facial expressions using spatio-temporal features, improving emotion recognition accuracy.

Contribution

It presents the first dataset of congruent and incongruent facial expressions and a novel deep learning approach for authentic emotion recognition.

Findings

Easier to distinguish genuine from unfelt expressions overall.

Certain emotion pairs like contempt and disgust are more challenging to differentiate.

Method improves state-of-the-art on CK+ and OULU-CASIA datasets.

Abstract

Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase.