Anonymizing Faces, Preserving Anomalies

Wed, 01 Oct 2025 00:00:00 +0000

Deep-Learning Based Privacy Protection for Data of Patients with Cleft Lip

Karen Rosero, Lucas M. Harrison, Alex A. Kane, Carlos Busso, Rami R. Hallac
15th International Congress on Cleft Lip/Palate and Related Craniofacial Anomalies (CLEFT 2025), Kyoto, Japan
In collaboration with UT Southwestern Medical Center and Children’s Health.

Why anonymize clinical photos?

Facial photographs support treatment planning for patients with cleft lip: lip marking, assessment of surgical outcomes, facial symmetry analysis, and automatic detection of lip abnormalities. Using facial images in medical contexts also raises critical ethical and legal concerns about patients’ privacy, which limits how these images can be shared for research and education.

Traditional anonymization falls short

Black bars over the eyes, pixelation and blurring are the usual ways to de-identify a face. Generative AI can reconstruct a highly similar face from any of them, so they do not reliably protect the patient.

Our approach: replace the face, keep the anomaly

We preserve the orofacial anatomy of the patient and use a generative AI system to synthesize the rest of the face. The result is a realistic portrait of a person who does not exist, with the clinically relevant region left exactly as it was.

Landmarks. Facial landmarks are detected automatically on the original image, using our (FG 2024).
Mask. A mask isolates the orofacial region of interest. It can be adjusted manually or personalized for other facial regions.
Synthesis. A diffusion model, guided by the orofacial region and a text prompt (for example, “light skin girl, green eyes”), generates the new face by progressive denoising. The region of interest stays unaltered, and the text controls the appearance of the synthetic face.

Results

The approach generalizes to both pre- and post-operative cleft lip cases and produces realistic, anatomically consistent synthetic faces.

We anonymized 140 images of patients with cleft lip, evenly distributed between pre- and post-operative cases, and asked two questions.

Is the generated face recognized as the same person? Each original image was compared with 8 synthesized variants using a deep-learning identity verification tool, for a total of 1,120 generated images. 98.9% were classified as distinct individuals. The remaining 1.1% were flagged as potential matches, all with low confidence (below 70%).

Is the orofacial region preserved? A clinical evaluator rated 50 images on a 1–5 scale, where 5 means high anatomical preservation after anonymization. 48 images received a 5 and the other 2 received a 4.

Takeaways

A privacy-preserving method for facial portraits of patients with cleft lip.
It retains the clinical features that matter for evaluation.
It makes it possible to build and share more diverse clinical datasets.

Images on this page are illustrations, images generated by our method, or original images blurred as in the conference presentation.