A generated image repository of aging faces.

Area of Science:

• Cognitive Psychology
• Social Psychology
• Computer Vision
• Human-Computer Interaction

Background:

• Person perception research heavily relies on facial stimuli.
• Existing face databases are limited, overrepresenting young, white individuals and young adult perceivers.
• This bias restricts the generalizability of findings in person perception studies.

Purpose of the Study:

• To develop an open-access, diverse database of AI-generated faces across multiple life stages.
• To provide age-normed and validated face stimuli for psychological research.
• To facilitate research on age comparisons and perceptions of aging.

Main Methods:

• Utilized advanced generative algorithms to digitally age 62 young individuals.
• Preserved identity-specific features while realistically simulating age-related changes.
• Created a database of 186 images representing young, middle-aged, and older adult stages for each individual.

Main Results:

• Generated a novel, open-access database of 186 AI-created facial images.
• The database features equal representation of males and females across three distinct age groups.
• All images were age-normed and validated for authenticity, ensuring research utility.

Conclusions:

• The AI-generated face database addresses critical limitations in current person perception research.
• This resource enables robust investigation into age comparisons and perceptions of aging.
• The database serves as a valuable tool for diverse research questions in behavioral science.