The free face scan described here processes a photo in your browser, according to its publisher, but its score is not an objective measure of beauty. The tool detects facial landmarks and applies a disclosed geometric formula; the landmarks locate features, while the formula determines how those measurements contribute to the result. The publisher says its paid report uses a different flow in which a photo is uploaded for processing.
What the face rater measures
The Attractiveness Report says its browser tool uses MediaPipe’s face landmarker to detect 478 points on a face. It then calculates a score from those coordinates using its own disclosed formula. The publisher describes the detector as a landmark finder, not an AI that independently decides whether someone is attractive; its AI Policy puts it this way: “The AI facial analysis on this site is a landmark detector, not a judge.”
The publisher calls the current formula Score v1.0. These are its weights—not a scientific consensus about what makes a face attractive:
| Measure | Weight in Score v1.0 |
|---|---|
| Symmetry | 30% |
| Facial thirds | 20% |
| Proportion | 20% |
| Facial fifths | 15% |
| Harmony | 15% |
The methodology says averageness and sexual dimorphism are not currently scored. The weights describe this publisher’s choices about which geometric measures to include and how much each contributes. They do not establish that the result is a universal beauty standard, or that 478 detected points validate the formula. The publisher’s explanation, How the AI Measures Attractiveness: Accuracy and Limits, was last reviewed August 29, 2026.
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Does your photo leave your browser?
For the free scan, the publisher says the photo is processed in the browser and is not uploaded, transmitted, or stored. It says the detected landmark coordinates and derived measurements remain in browser memory for the session. The publisher’s methodology page states: “Every measurement runs in your browser on a 478-point landmark mesh; no photo is uploaded, stored or transmitted, which also means we cannot see the face we scored.” These are the publisher’s stated practices, not the result of an independent security audit.
There is an important exception: the publisher’s privacy page says its paid report requires a photo upload for processing. It says that photo is deleted within 24 hours and is not used to train a model; it also says Stripe handles payment. The free-scan description should not be taken as a promise that the paid report keeps the photo on-device.
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Why two AI tests can give different scores
There is no universal scale for an AI face rating. A landmark-and-formula tool can differ from another rater in which points it detects, which proportions it calculates, how it weights those measurements, and how it maps them to a score. A general-purpose generative AI asked to judge a face may use a different, less explicitly defined basis. The photo itself also matters: pose, lighting, expression, framing, and image quality can change what a system detects or how it responds.
The Attractiveness Report says pose and lighting affect the reliability of a single scan. For a more consistent comparison, use similarly lit, frontal photos with a neutral expression and similar framing—but consistency does not make the resulting score objective. Optional equipment such as a phone ring light may help keep lighting more consistent; no accessory is required, and better lighting does not validate a rating or guarantee a higher one.
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The publisher does not publish a confidence band or percentile. It says it has not published a test-retest study or assembled a sufficiently large reference population. Without those, a single score should not be read as a precise ranking against other people or as evidence that a small change between scans is meaningful.
What published evidence can—and cannot—tell you
A 2024 study indexed by the National Library of Medicine compared five AI attractiveness-rating websites with a human focus group. It used 40 AI-generated images of adult, white, female faces shown with neutral expressions and frontal views. AI scores correlated positively with the group’s scores, but 30 of the 40 images received statistically higher scores from AI websites than from the focus group. The authors advised cautious use. The study, “Artificial Intelligence Versus Human Focus Group Rating of Facial Attractiveness”, did not validate this browser tool, and its narrow image sample does not establish performance across demographics.
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A 2026 focused review in the Journal of Medical Internet Research discusses demographic bias and dataset limitations in AI aesthetic facial evaluation and proposes ways to address them during development. That is field-level context, not an audit of this particular rater. The publisher also identifies dataset bias as a limitation in its methodology, but the sources do not establish how this tool performs across demographic groups.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a face-rating tool
Whether a rater uses a landmark formula or a general-purpose generative AI, the useful questions are about its method and limits—not whether it can produce a number:
- Is the method disclosed? Check whether the tool explains its measurements and weights, or whether its reasoning is opaque.
- Where does the photo go? Look for a clear account of local processing, uploads, retention, and any differences between free and paid features.
- Can the result be reproduced? Ask whether repeated scans under similar conditions have been tested, and whether the system reports uncertainty rather than implying false precision.
- What population informs the method? Find out whether the reference data and testing cover the people who may use it, and whether demographic performance has been assessed.
The reviewed sources do not establish that landmark-and-formula raters are universally more accurate than generative AI—or vice versa. Neither approach supplies an objective verdict on a person’s appearance or worth.
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