Guideline for Annotating Dentofacial Markers to Distinguish Real and Synthetic Faces

This document serves as the official annotation guideline for labeling dentofacial attributes in facial images, with the objective of distinguishing between real human portraits and AI-generated faces. It was developed as supplementary material for the research article "Dentofacial Markers for the Detection of Faces Generated by Diffusion-Based Text-to-Image Artificial Intelligence Models." The guideline defines a standardized set of 12 dentofacial markers—including tooth shape, eye gaze consistency, pupil symmetry, and skin pore visibility—and provides operational definitions, applicability criteria, and examples to ensure consistent and reproducible annotations. This resource is intended for use by researchers and annotators working on computational image analysis, deepfake detection, and medical forensics involving facial imagery.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-01
DOI
https://doi.org/10.5281/zenodo.15120829
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Guideline for Annotating Dentofacial Markers to Distinguish Real and Synthetic Faces

Zenodo (CERN European Organization for Nuclear Research)
Dental Radiography and Imaging
article

Guideline for Annotating Dentofacial Markers to Distinguish Real and Synthetic Faces

article en

Abstract

This document serves as the official annotation guideline for labeling dentofacial attributes in facial images, with the objective of distinguishing between real human portraits and AI-generated faces. It was developed as supplementary material for the research article "Dentofacial Markers for the Detection of Faces Generated by Diffusion-Based Text-to-Image Artificial Intelligence Models." The guideline defines a standardized set of 12 dentofacial markers—including tooth shape, eye gaze consistency, pupil symmetry, and skin pore visibility—and provides operational definitions, applicability criteria, and examples to ensure consistent and reproducible annotations. This resource is intended for use by researchers and annotators working on computational image analysis, deepfake detection, and medical forensics involving facial imagery.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 40%
Dental Radiography and Imaging
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