  This approach builds on two following findings in cognitive science: (i)
human cognition partially determines expressed behaviour and is directly linked
to true personality traits; and (ii) in dyadic interactions individuals'
nonverbal behaviours are influenced by their conversational partner behaviours.
In this context, we hypothesise that during a dyadic interaction, a target
subject's facial reactions are driven by two main factors, i.e. their internal
(person-specific) cognitive process, and the externalised nonverbal behaviours
of their conversational partner. Consequently, we propose to represent the
target subjects (defined as the listener) person-specific cognition in the form
of a person-specific CNN architecture that has unique architectural parameters
and depth, which takes audio-visual non-verbal cues displayed by the
conversational partner (defined as the speaker) as input, and is able to
reproduce the target subject's facial reactions. Each person-specific CNN is
explored by the Neural Architecture Search (NAS) and a novel adaptive loss
function, which is then represented as a graph representation for recognising
the target subject's true personality. Experimental results not only show that
the produced graph representations are well associated with target subjects'
personality traits in both human-human and human-machine interaction scenarios,
and outperform the existing approaches with significant advantages, but also
demonstrate that the proposed novel strategies such as adaptive loss, and the
end-to-end vertices/edges feature learning, help the proposed approach in
learning more reliable personality representations.
