What actually happens
to your selfie
No invented numbers, no vague "AI magic." Here's the real pipeline, in the order it runs.
Face detection & alignment
Our proprietary engine maps 450+ landmark points on your face, and more. If your head is tilted, the image is rotated back level using your eye positions before anything else runs — a tilted photo otherwise throws off every zone that comes after it. The photo must show one face, filling at least 5% of the frame, at a small enough angle that the read stays reliable.
Five zones, not a grid
Early versions mapped a fixed grid onto the photo. That broke at even a 5° head tilt — grid cells drifted onto the wrong part of the face. Zones are now built from the landmarks themselves: T-zone, left cheek, right cheek, chin, and under-eye, each a real region anchored to your actual bone structure, not a rectangle guessed from the frame.
Skin segmentation & scoring
A dedicated in-house segmentation model separates skin from hair, eyes, lips, and background. Within each zone, small patches of real skin are scored across five traits — oiliness, dryness, acne, pigmentation, sensitivity — using computer-vision measurements (texture, color variance, light reflectance), not a single black-box number.
Acne gets a second, dedicated model
Acne severity is refined by a purpose-built in-house classifier — a small neural network trained specifically to grade acne lesions by severity, run on cropped patches from each zone. It overwrites the general texture-based estimate with a purpose-built one.
Facial hair is excluded, not guessed through
A separate classifier detects facial hair regions and excludes them from acne, dryness, pigmentation, and sensitivity scoring in that zone, rather than scoring stubble as if it were skin texture. A zone under facial hair is honestly reported as unmeasured, not silently guessed.
A confidence score that can downgrade the routine
Photo quality, blur, cross-zone consistency, and per-zone detector reliability combine into one confidence score. If it falls below a threshold, every score is zeroed and the routine trims to maintenance-only — the system is built to say 'we're not sure' rather than hand out a confident-sounding routine from an unreliable photo.
Ingredients matched to what was actually measured
Your five axis scores are matched against a database of ingredients tagged by which concern they address, ranked, and filtered for conflicts (see the ingredient guide) before being assembled into an AM/PM routine, with a search link for each recommended ingredient that resolves to your own country's Amazon.
Questions people actually ask
Is Skinzy a medical diagnosis?
No. Skinzy is a cosmetic skin-analysis tool, not a diagnostic device. It scores five visible skin traits from a photo and suggests skincare ingredients — it doesn't diagnose, treat, or prevent any medical condition. See a dermatologist for anything that looks or feels like more than a cosmetic concern.
What does Skinzy actually measure?
Five zones on your face — T-zone, both cheeks, chin, and under-eye — each scored on five traits: oiliness, dryness, acne, pigmentation, and sensitivity. Our engine maps 450+ facial landmarks and more to locate the zones precisely; separate models then score each trait.
How accurate is the confidence score?
The confidence score (shown as a percentage, typically 70-81%) blends photo quality, blur, how consistent the scores are across zones, and how reliable each detector was on your specific photo. Below a threshold, Skinzy trims the routine to maintenance-only rather than guessing.
Does Skinzy work for all skin tones?
Not equally well yet. Pigmentation looks for spots visibly darker and browner than the skin right around them and ignores pores, fine lines and stubble, so it does not depend on guessing your skin tone from the photo. But it still under-counts small dark spots on deeper skin tones, and we have tested on too few deeper skin tones to claim the other traits work equally well. This is a known limitation, not a solved problem.
Guides
All guidesWhat Is My Skin Type? How to Determine Your Skin Accurately
Most online quizzes force your whole face into one single label. Real faces have distinct zones with vastly different oil and hydration profiles.
How to Build a Skincare Routine: A Phased, Evidence-Based Guide
Starting five new products at once is the fastest way to destroy your moisture barrier. Learn the 4-phase introduction schedule used by Skinzy.
What Is AI Skin Analysis? How Computer Vision Analyzes Skin
Beyond marketing quiz wrappers: how real computer vision, facial landmark tracking, and specialized classifiers evaluate skin from a photo.
Is AI Skin Analysis Accurate? An Honest Answer
Most tools answer this with a marketing number. Here is what our scanner measures well, what it measures poorly, and where it refuses to answer at all.
AI Skin Analysis vs a Dermatologist: What Each Is Actually For
These are not competitors. One measures visible surface properties continuously and cheaply; the other diagnoses disease. Confusing them is how people delay care.
How to Take a Selfie for a Skin Scan
Skinzy will not let you submit a photo it cannot read. This is what the camera is checking while you hold still, in the same numbers the code uses.
How to Read Your Skin Score Report
Every number on the report is a severity measurement, not a grade. Lower is better, and a 2.4 is an ordinary, healthy result.
Why Your Skin Scan Results Change Between Scans
Two photos of the same face, minutes apart, will not score identically. Here is how much they actually differ, measured rather than asserted.