| Autor: |
Siyang Song, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar, Hatice Gunes |
| Přispěvatelé: |
Song, Siyang [0000-0003-2339-5685], Jaiswal, Shashank [0000-0003-4678-1511], Shen, Linlin [0000-0003-1420-0815], Valstar, Michel [0000-0003-2414-161X], Apollo - University of Cambridge Repository |
| Rok vydání: |
2023 |
| Předmět: |
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| Popis: |
—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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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