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Start the quiz →AI measures the geometric features that face-perception research links to attractiveness. Here's what it's actually measuring, what the limits are, and how to read your number honestly.
"Am I attractive?" is one of the most-searched questions on the internet. The honest answer is that it isn't one number — it's a combination of geometric features, skin quality, dynamic cues, and context, and people are systematically bad at rating themselves on any of it. AI face analysis can help with the geometric and skin part: it measures facial landmarks and proportions and compares them to anthropometric reference distributions and to the features face-perception research consistently links to higher attractiveness ratings. It can't tell you who you are. It can give you a clearer picture of what your face is actually doing in a photo. If you want a comprehensive breakdown across multiple metrics with specific recommendations, try our free AI looksmaxxing test.
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Upload a selfie. AI measures the geometric features face-perception research links to attractiveness ratings, and gives you specific notes on what each one is doing.
Take the attractiveness test →Decades of face-perception research converge on a short list of features that predict attractiveness ratings across rater pools. These aren't our claims — they're the ones that have replicated. Our AI measures all of them. Each entry below points to the original work and gives a fair sense of how strong the effect is.
Faces closer to the population average of their group are consistently rated more attractive — Langlois & Roggman (1990) is the foundational paper, and the effect has held up under repeated replication. "Average" here doesn't mean dull; it means proportionally typical, which is a strong signal of developmental stability.
Bilateral symmetry has a small-to-moderate effect on attractiveness ratings — Rhodes (2006) and Little/Jones/DeBruine (2011) are the standard references. It matters, but the effect is more modest than internet discourse usually suggests, and perfect symmetry is statistically rare. Test your symmetry here.
Skin texture and evenness are surprisingly strong predictors of attractiveness ratings — Fink, Grammer & Matts (2006) and Jones et al. (2004) isolated skin homogeneity as a major signal. This is also one of the most addressable features: consistent cleansing, barrier care, and tone-evenness work translate to visible change on photo timelines of weeks.
Cues like jaw definition and brow ridge in male faces, and softer features in female faces, contribute to perceived attractiveness — Perrett et al. (1998) is the canonical reference. The relationship isn't monotonic; preferences shift with rater group and context. Test your jawline.
The contrast between facial features and surrounding skin is itself an attractiveness cue — Russell (2003, 2009) showed this clearly. It's also why lighting matters so much: the same face under flat versus directional light produces very different contrast reads.
Canthal tilt, palpebral fissure dimensions, and the upper-lid/orbital rim relationship contribute to how the eye area reads. Cunningham's cross-cultural work (1986, 1995) consistently identified eye-region features as a major rater signal. Sleep quality and under-eye condition also show up here on short timelines. Test your eye area.
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AI measures the geometric features that perception research actually anchors to and tells you which are working in your photos.
Take the Attractiveness Test →Free · No signup · Instant results · 17 metrics · NIH-cited landmarks
Most "Am I attractive?" quizzes ask you to self-assess: "How would you rate your jawline?" The problem is that self-rating is unreliable in both directions. Alicke & Govorun (2005) document the better-than-average effect, where most people overrate themselves on socially valued traits. The same body of work shows that people who feel insecure on a given day systematically underrate themselves. Either way, a self-assessment quiz is measuring your mood, not your face.
AI face analysis sidesteps that. Computer vision detects facial landmarks at sub-pixel precision, calculates ratios and angles directly, and compares them against anthropometric reference distributions and the feature dimensions perception research has identified. The output is a measurement, not a feeling — closer to "your gonial angle is X°" than "you're a 7".
Two honest caveats. First, AI inherits its training data — Buolamwini and Gebru (2018, Gender Shades) showed that face-analysis models perform very differently across demographic groups, and that warning applies here. Second, AI is sensitive to photo conditions: Paskhover and colleagues (2018, JAMA Facial Plastic Surgery) showed that selfies taken at typical short distances distort facial proportions. Standardize your shots and your numbers stabilize.
Within those caveats, the upside is specificity. Instead of "I feel like my jaw is weak," you get a measurement you can re-take in two months and compare. Our professional face audit summary packages all the metrics into a single deliverable you can re-rate against later under the same conditions.
The point of metric-based analysis isn't to assign a verdict — it's to identify which levers are real and in what order to pull them. Sorting them honestly:
Skin condition (weeks)
The fastest-moving and best-supported lever in the perception literature. A barrier-friendly cleanser, an active like niacinamide, and consistent sun protection address the texture and tone signals raters respond to. See our skincare guide.
Soft-tissue from body composition (months)
Face fat tracks total body fat. A modest sustained deficit (Helms 2014; Trexler/Smith-Ryan/Norton 2014) with adequate protein (Morton 2018) is what produces the visible facial changes in real before-and-afters. There is no spot-reduction shortcut for face fat. See our jawline guide.
Sleep, hydration, sodium (days)
Facial puffiness and under-eye condition respond on a 24–72-hour timeline. This is what people often mistake for "jawline progress" in their first week. Real, but not the structural change they think it is.
Bone-anchored structure (mostly fixed in adults)
Adult facial bone is largely set. The published evidence for self-directed posture interventions changing bone position is weak. Mewing's honest framing is "maintained tongue posture and reduced mouth-breathing have non-zero effects," not "you can grow a new jaw at 28."
The emotional return on actually working the first two — skin and body composition — over a few months is consistently larger than people expect, and almost always larger than what they get from chasing structural promises. The data point you want from a face score is "is the lever I'm working actually moving?" — not "am I a 7 now?"
Geometric measurements, anchored to the perception literature. One selfie, specific notes per metric.
✨ Take the Attractiveness Test →AI measures facial geometry — symmetry, proportions, jaw angle, canthal tilt, skin texture — and compares them to anthropometric reference distributions (Farkas, 1994) and to the features face-perception research links to attractiveness ratings (Rhodes, 2006; Langlois & Roggman, 1990). It produces measurements, not a verdict on you.
A short, replicated list: averageness (Langlois & Roggman, 1990), symmetry with a small-to-moderate effect (Rhodes, 2006; Little/Jones/DeBruine, 2011), sex-typicality (Perrett, 1998), skin condition (Fink/Grammer/Matts, 2006; Jones, 2004), and facial contrast (Russell, 2003, 2009). The combination matters more than any single feature.
Self-rating quizzes are unreliable — Alicke & Govorun (2005) on the better-than-average effect documents this directly. AI is more objective for geometry but inherits training-data bias (Buolamwini & Gebru, 2018) and is sensitive to photo conditions (Paskhover, 2018, JAMA Facial Plastic Surgery).
Skin condition responds within weeks. Soft-tissue change from body composition responds across months. Bone-anchored structure is largely fixed in adults. Honest progress comes from the right lever in the right order.
Note: Attractiveness is multidimensional and includes factors beyond facial geometry — confidence, voice, expression, dynamic motion, context, social warmth. AI face analysis measures the static geometric component only. Use results as one data point, not a definitive judgment of you.
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For overall facial geometry
Start free
See your overall score + the 2 metrics dragging your face down. Instant. No signup.
Run free scan →Recommended
Every metric scored, percentile-ranked against the population, with a 30-day glow-up plan. Instant PDF unlock.
Unlock full report · $14.99 →Or full report
Human-grade written report. Use it for LinkedIn, dating, or anywhere a written analysis beats raw metrics.
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3 quick questions → we recommend the right product for your goal, timeframe, and budget.
Start the quiz →Done reading? Get your photos audited
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Built RealSmile after testing every face analysis tool and finding most give fake scores with no methodology. Background in computer vision and TensorFlow.js. Has analyzed peer-reviewed reference data and published open research data on facial metrics.