Learning Person-specific Cognition from Facial Reactions for Automatic Personality Recognition

• Published in: IEEE transactions on affective computing Vol. 14; no. 4; pp. 1 - 18
• Main Authors: Song, Siyang, Shao, Zilong, Jaiswal, Shashank, Shen, Linlin, Valstar, Michel, Gunes, Hatice
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognition; Cognition & reasoning; Computer architecture; Dyadic interaction; End-to-end graph representation learning; Face recognition; Facial reaction generation; Feature extraction; Graph representations; Graphical representations; Learning; Multi-dimensional edge feature; Person-specific cognition simulation; Personality; Personality traits; Reliability; Target recognition; Task analysis; True personality recognition; Visualization
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2022.3230672

This paper proposes to recognise the true (self-reported) personality traits from the target subject's cognition simulated from facial reactions. This approach builds on the following two 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's behaviours. In this context, we hypothesise that during a dyadic interaction, a target subject's facial reactions are driven by two main factors: their internal (person-specific) cognitive process, and the externalised nonverbal behaviours of their conversational partner. Consequently, we propose to represent the target subject's (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 help in learning more reliable personality representations.