  Personality determines a wide variety of human daily and working behaviours,
and is crucial for understanding human internal and external states. In recent
years, a large number of automatic personality computing approaches have been
developed to predict either the apparent personality or self-reported
personality of the subject based on non-verbal audio-visual behaviours.
However, the majority of them suffer from complex and dataset-specific
pre-processing steps and model training tricks. In the absence of a
standardized benchmark with consistent experimental settings, it is not only
impossible to fairly compare the real performances of these personality
computing models but also makes them difficult to be reproduced. In this paper,
we present the first reproducible audio-visual benchmarking framework to
provide a fair and consistent evaluation of eight existing personality
computing models (e.g., audio, visual and audio-visual) and seven standard deep
learning models on both self-reported and apparent personality recognition
tasks. Building upon a set of benchmarked models, we also investigate the
impact of two previously-used long-term modelling strategies for summarising
short-term/frame-level predictions on personality computing results. The
results conclude: (i) apparent personality traits, inferred from facial
behaviours by most benchmarked deep learning models, show more reliability than
self-reported ones; (ii) visual models frequently achieved superior
performances than audio models on personality recognition; (iii) non-verbal
behaviours contribute differently in predicting different personality traits;
and (iv) our reproduced personality computing models generally achieved worse
performances than their original reported results. Our benchmark is publicly
available at \url{https://github.com/liaorongfan/DeepPersonality}.
