Beauty Re-defined: A Comparative Analysis of Artificial Intelligence-Generated Ideals and Traditional Standards

Background Traditional methods for assessing facial beauty rely on subjective measures like averages or "golden ratios." However, artificial intelligence (AI) offers a data-driven approach to analyzing attractiveness. This study explores how AI-generated beauty criteria compare to established ideals, considering cultural influences and the evolving concept of beauty. Methods To explore how AI-generated beauty ideals compare to traditional standards, we used three AI text-to-image generation tools (Dezgo (Dezgo SAS LLC, France), Freepik (FreePik Company, Malaga, Spain), and ImagineArt (Vyro, Islamabad, Pakistan)) to create images from a specific prompt. The first four generated images for each gender that met our criteria were included in this study. A single researcher used MediaPipe Studio software to identify ten key facial landmarks on each image. Landmark distances were measured twice in Adobe Photoshop 2023 (Adobe, San Jose, California, United States) and averaged for each measurement. The average values were then used to calculate 23 facial proportion ratios based on established neoclassical canons and golden facial ratios. We then compared these AI-generated ratios to the ideal values using one-sample t-tests in IBM SPSS Statistics for Windows, Version 29 (Released 2023; IBM Corp., Armonk, New York, United States), p < 0.05 significance, to assess alignment with traditional beauty standards. Results AI-generated faces displayed statistically significant differences, p < 0.05, from established beauty standards in both neoclassical canons and golden ratios for both males and females. Differences were seen in facial width, upper and lower face proportions, and eye symmetry. Conclusion AI-generated faces deviated from traditional beauty standards of neoclassical canons and golden ratios for both genders. This suggests AI incorporates factors beyond established ideals, potentially reflecting contemporary preferences, cultural biases, or emerging trends.