Résumé: Personality describes an individual's internal attributes, encompassing distinctive thoughts, emotions, and behaviour. Various studies have been undertaken to quantify these personality traits using externally visible cues and measures. Despite the possibility of a disparity between the apparent and real personality traits, researchers have identified various deep learning techniques to measure the apparent personality as part of the ChaLearn Looking at People, ECCV challenge. We provide an overview of the different deep learning models used for apparent personality detection. The primary objective of previous studies was to develop highly accurate prediction models, and subsequently, the focus shifted towards interpreting the output of these techniques. Although explainable AI (XAI) techniques have shown that facial features are essential in predicting personality, the consistency of these results is still a critical factor to consider. Moreover, the majority of studies utilized the ECCV challenge CVPR'17 dataset to predict apparent personality; therefore, there is a requirement to validate these findings through additional datasets. In conclusion, ample scope exists for further research in the domain of personality detection that can yield highly accurate and more interpretable models.

Langue: Anglais