Designing A Physiognomy-Based Comic Character Model: Bridging Facial Features and Personality Traits Through Generative AI Workflow

Novice comic artists often struggle to design distinctive and memorable characters, frequently relying on intuition, which can lead to repetitive archetypes. This research examines how principles historically associated with physiognomy can be systematically applied to link specific facial features (e.g., eye shape, jawline) with desired perceived personality traits (e.g., conveying an impression of a perfectionist or a brave individual), thereby providing a solution to this design challenge. While early artists like Töpffer intuitively manipulated facial features to craft character expressions, a modern, systematic approach has been lacking. This study proposes a structured workflow that leverages an understanding of how facial cues influence first impressions. When inferring personality traits from facial features, observers often show consensus rather than asserting definitive links between features and actual traits. This workflow visualises these feature-impression relationships by utilising Visual Generative AI. This method provides initial visual references, simplifying the translation of abstract trait perceptions into concrete visual forms that artists can refine according to their styles. This model aims to enhance character distinctiveness and expressiveness by employing a qualitative approach that integrates literature on first impressions, identifies commonly perceived physiognomic cues, and utilises AI-assisted visualisation techniques. The main contribution is a standardised, practical guideline that empowers novice artists by strategically using facial features as visual cues to effectively shape initial audience perceptions of a character's personality, grounded in the psychology of first impressions rather than outdated physiognomic determinism.