Continuous Affect Prediction using Eye Gaze

TL;DR

This paper investigates the use of eye gaze as a sole modality for continuous affect prediction, demonstrating superior valence prediction performance and significantly fewer features compared to speech-based systems.

Contribution

It is the first to evaluate eye gaze as a unimodal input for continuous affect prediction, showing its effectiveness over speech features in valence prediction.

Findings

Eye gaze outperforms speech features in valence prediction.

The proposed eye gaze feature set has 98% fewer features than speech-based sets.

Eye gaze achieves better correlation results for valence prediction.

Abstract

In recent times, there has been significant interest in the machine recognition of human emotions, due to the suite of applications to which this knowledge can be applied. A number of different modalities, such as speech or facial expression, individually and with eye gaze, have been investigated by the affective computing research community to either classify the emotion (e.g. sad, happy, angry) or predict the continuous values of affective dimensions (e.g. valence, arousal, dominance) at each moment in time. Surprisingly after an extensive literature review, eye gaze as a unimodal input to a continuous affect prediction system has not been considered. In this context, this paper evaluates the use of eye gaze as a unimodal input to a continuous affect prediction system. The performance of continuous prediction of arousal and valence using eye gaze is compared with the performance of a speech system using the AVEC 2014 speech feature set. The experimental evaluation when using eye gaze as the single modality in a continuous affect prediction system produced a correlation result for valence prediction that is better than the correlation result obtained with the AVEC 2014 speech feature set. Furthermore, the eye gaze feature set proposed in this paper contains 98% fewer features compared to the number of features in the AVEC 2014 feature set.