3D facial analysis shows biological basis for gender-affirming surgery

An example of a subject (center column) that has been transformed to have a more masculine (right) and feminine (left) shape, leaving the overall size unchanged. The same shape transformation process applied to the nose region of the same subject. Picture by Rahul Seth

Gender-affirming facial surgery (GFS) is performed by transgender people who want facial features that better reflect their gender identity. Until now, there have been few objective guidelines to justify and facilitate effective surgical decision-making for gender-affirming facial surgery.

To validate surgical decisions for GFS, researchers from UC San Francisco and the University of Calgary set out to quantify the effect of gender on adult facial size and shape through image analysis of three-dimensional (3D) facial surface.

In a study published online this week in Facial plastic surgery and aesthetic medicine, the investigators undertook a surgical analysis of 3D facial size and shape to quantify and visualize facial gender differences. Their results reveal significant differences in the shape and size of male and female craniofacial features and provide data-based anatomical guidance and rationale for GFS, particularly for forehead contour cranioplasty, mandible and back alterations. chin, rhinoplasty and cheek modifications.

“Our goal was to establish an important, definitive, and biological relationship between facial features and gender. This allows the patient to navigate toward a facial appearance that matches their gender identity, and thus reduces gender errors and gender dysphoria while enhancing self-perception,” said lead author Rahul Seth, MD, associate professor of facial plastic and aesthetic surgery in the UCSF Department of Otorhinolaryngology-Head and Neck Surgery. “We believe this data provides surgeons, patients and insurance payers with realistic, surgery-oriented analysis of 3D facial size and shape to guide patients and surgeons in performing these complex and life-changing surgeries.”

Forehead, jaw, nose and cheek have the greatest gender differences

The researchers were able to determine that, on average, male faces are 7.3% larger than female faces. Gender was associated with significant differences in facial shape across the entire face as well as in each subregion considered in the study. The areas of the face where gender has the greatest effect on shape are the forehead, jawline, nose and cheek. The authors therefore provide supporting evidence and guidelines for appropriate modifications of these facial areas for GFS, although each patient’s goals and face are unique.

To obtain these results, the researchers obtained facial measurements by applying a facial surface atlas to 3D surface scans of 545 men and 1028 women over the age of 20. Differences between male and female faces were analyzed and visualized for a set of predefined surgically relevant facial regions.

3D renderings of a subject's facial landscape with areas on the forehead, forehead, nose, cheek, lips, chin, and jawline marked in red to visualize facial landmarks.
The facial regions and landmarks used in this study visualized on a sample subject. Area regions: (A) forehead, (B) forehead, (C) nose, (D) cheek, (E) lips, (F) chin, (G) jaw. Picture by Rahul Seth
3D heat map showing a subject's face in three different views: frontal, tilted and side view.  The average female face shape is colored based on the difference between the average male and female face shapes.  A key on the right indicates the distance in millimeters, with blue being 3.00 millimeters, light blue being 1.5 millimeters, green being 10.0 millimeters, yellow being -1.50 millimeters and red being - 3.00 millimeters.  On the face map, the forehead and bridge of the nose are blue, the cheeks are red, and the rest of the face is green.
The average female face shape colored based on the difference between the average male and female face shapes. Picture by Rahul Seth

Each scan used a 3D surface mesh made up of 3D vertices connected by triangles. Triangle meshes were used to represent surface data numerically. While the exact count per face differed slightly in the raw data, each mesh contained 27,903 vertices to provide consistent, high-resolution facial data. Concepts essential to rigorous geometric morphometry were applied to surface data to provide detailed results.

To analyze the effects of gender on facial size and shape, a variety of facial subregions were first specified based on potential for surgical application and relevance for surgical decision-making and planning. The regions included both large surface sections of the face (cheeks, nose) as well as axial or sagittal curves. Dense surface-based measurements and shape, size, and shape comparisons were performed for the entire face and facial subregions.

The researchers plotted their findings in a “heat map” to show the average difference between male and female facial shape differences. Using this information, these changes were applied to an example face to demonstrate their validity for “surgically” altering the face along an axis of masculinity and femininity.

While the study provides important differentiators for differences in male and female facial size and shape, the results also suggest that in some cases, effective sex modification can be achieved by over-correcting size or shape. For example, if functional issues prevent a surgeon from downsizing the jawline, it may be ideal to contour the area to be less square, emphasizing a more feminine shape.

The results of this study provide anatomical rationale and guidance that may enable optimal GFS outcomes for transgender and gender-diverse people.

Authors: First author is Jordan Bannister, BASc, University of Calgary, Graduate Program in Biomedical Engineering. Co-authors are Hailey M. Juszczak, BA, and P. Daniel Knott, MD, UCSF, Division of Facial Plastic and Reconstructive Surgery, Department of Otorhinolaryngology-Head and Neck Surgery; J. David Aponte, MSc, David C. Katz, PhD, and Benedikt Hallgrimsson, PhD, Department of Cell Biology and Anatomy, Alberta Children’s Hospital Research Institute, University of Calgary; Seth Weinberg, University of Pittsburgh, Department of Oral and Craniofacial Sciences, School of Dentistry; Nils D. Forkert, PhD, University of Calgary, Department of Radiology, Alberta Children’s Hospital Research Institute and Hotchkiss Brain Institute, Cumming School of Medicine.

Funding: This research was funded by OFS and the National Institutes of Health (U01-DE024440) and the Canada Research Chairs program.

Disclosures: Authors JJB, JDA, DCK, BH, NDF, and RS are affiliated with Deep Surface AI, Inc., a company specializing in 3D facial analysis and morphing.

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