  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.
