  Continuous affect prediction involves the discrete time-continuous regression
of affect dimensions. Dimensions to be predicted often include arousal and
valence. Continuous affect prediction researchers are now embracing multimodal
model input. This provides motivation for researchers to investigate previously
unexplored affective cues. Speech-based cues have traditionally received the
most attention for affect prediction, however, non-verbal inputs have
significant potential to increase the performance of affective computing
systems and in addition, allow affect modelling in the absence of speech.
However, non-verbal inputs that have received little attention for continuous
affect prediction include eye and head-based cues. The eyes are involved in
emotion displays and perception while head-based cues have been shown to
contribute to emotion conveyance and perception. Additionally, these cues can
be estimated non-invasively from video, using modern computer vision tools.
This work exploits this gap by comprehensively investigating head and eye-based
features and their combination with speech for continuous affect prediction.
Hand-crafted, automatically generated and CNN-learned features from these
modalities will be investigated for continuous affect prediction. The highest
performing feature sets and feature set combinations will answer how effective
these features are for the prediction of an individual's affective state.
