The free face scan described by The Attractiveness Report runs in the browser, where its publisher says the photo is not uploaded, transmitted, or stored. But “on-device” describes where the calculation happens—not whether an attractiveness score is objective. The tool detects facial landmarks, applies a disclosed weighted geometry formula, and returns a score shaped by those choices and by the photo itself.
What “on-device” means for this face rater
The Attractiveness Report says its free scan uses MediaPipe’s face landmarker to map 478 facial landmarks in the browser. It then calculates measurements from those points. The publisher’s AI policy summarizes the distinction: “The AI facial analysis on this site is a landmark detector, not a judge.” (AI Policy, last reviewed September 1, 2026.)
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In other words, the landmark model identifies locations on a face; the publisher’s formula turns their coordinates into a score. The existence of 478 points does not, by itself, establish that the chosen measurements or weights are a scientifically validated definition of attractiveness.
The disclosed Score v1.0 formula
The publisher lists five components and their weights:
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| Component | Weight in Score v1.0 |
|---|---|
| Symmetry | 30% |
| Facial thirds | 20% |
| Proportion | 20% |
| Facial fifths | 15% |
| Harmony | 15% |
These are the publisher’s disclosed formula weights, not a research consensus. Its methodology says averageness and sexual dimorphism are not currently scored. It also does not publish a percentile or confidence band, saying it has not published a test-retest study or established a sufficiently large reference population. (Methodology, last reviewed August 29, 2026.)
Does the photo leave your browser?
For the free scan, the publisher says the photo stays in the browser: it is not uploaded, transmitted, or stored. It says landmark coordinates and derived measurements remain in browser memory for the session. The methodology page puts it this way: “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.” (The Attractiveness Report methodology.) These are the publisher’s stated practices; the sources reviewed do not independently audit the tool’s implementation or security.
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There is an important exception: the publisher describes a different flow for its paid report. Its privacy page says a photo is uploaded for processing, deleted within 24 hours, and not used to train a model; it also says Stripe handles payment. Do not extend the free-scan, in-browser claim to that paid service. (Privacy page, last reviewed August 29, 2026.)
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What does an AI face rating actually measure?
Here, it measures the output of selected geometric calculations over detected facial points. That can make the method more inspectable than an unexplained score, but it does not make the result a universal standard for beauty or a measure of a person’s worth. The score depends on which features are measured, how they are weighted, and how well the input image lets the system locate them.
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The publisher says pose and lighting affect the reliability of a single scan. For a comparison between scans, keeping the camera angle, expression, distance, and lighting similar can reduce avoidable input differences; it cannot establish that the formula is valid. The publisher’s decision not to provide confidence bands or percentiles also means the displayed number should not be read as a calibrated rank or uncertainty estimate.
Why do two AI tests give me different scores?
There is no single shared scale implied by the word “AI.” A landmark-and-formula rater and a general-purpose generative AI asked to judge a face may use different methods, features, weights, and reference assumptions. They may also respond differently to pose, lighting, expression, or image framing. Unless a service discloses a common scale and validated comparison method, different outputs are not directly comparable—and neither should be treated as an objective verdict.
Rank #4
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| Question to check | Why it matters |
|---|---|
| Is the method disclosed? | A published formula lets readers see what contributes to the number; it does not prove the formula is valid. |
| Does the photo get transmitted? | Processing location and retention practices differ by service and may differ between free and paid features. |
| Can the result be reproduced? | Different scales, landmark choices, weights, and input conditions can change outputs. |
| What population informs the score? | A score’s interpretation depends in part on the people and data used to build or calibrate the method. |
| Are demographic performance and uncertainty reported? | Without these details, readers cannot infer equal performance across groups or how much confidence to place in an individual result. |
What research says about AI attractiveness ratings
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 adult, white, female faces with neutral expressions and frontal views. AI ratings were positively correlated with the human group’s scores, but 30 of the 40 images received statistically higher scores from the AI websites. The authors advised cautious use. This narrow study does not validate The Attractiveness Report’s specific tool, establish performance across demographics, or show that AI scores are interchangeable with human judgments. (PubMed study, 2024.)
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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. It offers field-level context, not an audit or performance result for this browser rater. (JMIR focused review, 2026.)
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How to use a score without overreading it
- Treat it as one tool’s output under one formula, not a universal beauty standard or a judgment of personal value.
- Read the method and privacy terms for the exact feature you use; a free browser scan and a paid report can handle photos differently.
- If comparing scans, keep pose and lighting consistent, while recognizing that consistency does not validate the scoring method.
- Do not infer a percentile, confidence range, or demographic fairness from a score when the service does not report those measures.
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