What this methodology covers
Short answer: ColorNote’s free web test does not perform face recognition or send the whole image to generative AI. The current browser reads image pixels locally, the user identifies three regions, and the service applies a rules-based calculation to three color values.
The free web test
This is the public, no-login four-season test at colornote.top/color-test-en.html. We checked the page script, request contract, and server route on August 25, 2026.
The complete ColorNote app analysis
The app has a separate capture guide, account flow, history, and photo-retention choices. Those are governed by the Privacy Policy and the notice shown before app analysis.
What the browser does from photo to three RGB samples
After you choose a photo, FileReader reads it as a Data URL inside the browser.
The longest side is reduced to at most 1,500 pixels, then drawn to an offscreen canvas.
You tap representative cheek skin, natural hair, and the colored part of the iris.
Each tap reads a 3×3 neighborhood and averages RGB values to reduce single-pixel noise.
The browser submits three RGB objects—not the image file or its Data URL—to the season endpoint.
- skin
- { r: 0–255, g: 0–255, b: 0–255 }
- hair
- { r: 0–255, g: 0–255, b: 0–255 }
- eye
- { r: 0–255, g: 0–255, b: 0–255 }
The field names are skin, hair, and eye. The server validates every channel as an integer from 0 through 255. The analysis body has no file, image URL, EXIF, name, email, or account-identifier field.
How the server chooses a basic four-season direction
The public endpoint reuses ColorNote’s deterministic rule algorithm; it does not call a large language model. The algorithm derives undertone, depth, clarity, and color contrast from the three samples. The public web result exposes only the base season.
It compares normalized red–blue relationships across the regions. Current weights are 50% skin, 30% iris, and 20% hair. Normalization reduces the influence of simple exposure shifts on raw channel differences.
It combines mean brightness across skin, iris, and hair rather than using skin alone. The algorithm also calculates clarity and the largest brightness contrast among the three pairs.
The endpoint returns a base season, localized names, a short description, an icon, and a fixed starter palette. It deliberately omits internal sub-season and evidence fields.
| Undertone direction | Depth direction | Basic web result |
|---|---|---|
| Warm | Light | Spring |
| Cool | Light | Summer |
| Warm | Deep | Autumn |
| Cool | Deep | Winter |
These thresholds are current ColorNote product rules, not a medical standard or a universal personal-color industry standard. A material rule change should trigger a new implementation review and modification date.
How the photo, request, and result are handled
The file and Data URL are used for preview and Canvas sampling on the current page. The analysis request has no photo field.
The public analysis route uses no account authentication or database session. It does not save the photo or create a retrievable personal analysis record.
The service uses the request IP for an in-memory anti-abuse window. The analysis route itself does not write that rate-limit bucket to the application database.
The page separately reports account-unlinked view, start, result-view, and App Store click events for aggregate product-funnel measurement.
The result-view event includes the basic season, interface language, and source label. It contains neither the photo nor the three RGB samples, and the web quiz does not attach an authenticated account token. It is not a retrievable personal color report. See the ColorNote Privacy Policy for the broader data rules.
Known limits: the algorithm sees photo pixels
- Illumination changes pixels. Warm lamps, cool daylight, colored-wall reflections, and screen fill light can change samples from the same person.
- White balance and camera processing change color. Device, capture mode, exposure, Photographic Styles, filters, and edits may shift tone, temperature, or saturation.
- Manual taps introduce location error. Cheek highlights or redness, shadows, dyed-hair reflections, colored contacts, and the iris–pupil boundary all affect values.
- Three local averages cannot represent a complete appearance. There is no physical draping sequence, controlled light, full skin-color distribution, or consultant observing continuous changes.
- A season label is a simplified direction. People near a boundary may move between adjacent seasons as conditions change. Treat the result as a starting point for comparison, not an identity.
For more comparable inputs, face a window in soft daylight, turn off filters and beauty effects, and use the same device at a similar time. Then check the direction with real garments or drapes under the same light. This test cannot replace a professional consultation under controlled lighting and must never be used for medical, skin-health, or identity decisions.
How anyone can inspect the behavior
- Open the browser’s Network panelClear it before testing and filter for the POST request to
/api/quiz/analyze. - Select a photo and complete all three tapsInspect the analysis payload. It should contain only the skin, hair, and eye RGB objects.
- Separate analysis from metrics
/api/metrics/eventsis a different product-use request. It should not contain the photo or the three RGB samples. - Test input sensitivityRepeat nearby taps under the same conditions, then use a photo under different light. The change makes sampling and lighting limits visible.
Official and primary sources that directly support this page
External sources explain the browser APIs and camera color processing. ColorNote’s exact data flow comes from the product implementation and its first-party public policies.
- W3C File APISupports: how File, FileList, and FileReader let a web app read a user-selected local file, including
readAsDataURL(). - WHATWG HTML Standard — Canvas pixel manipulationSupports: how Canvas 2D
getImageData()reads bitmap pixels and handles color-space conversion. - Apple Developer — ambientColorTemperatureSupports: ambient color temperature based on camera white-balance compensation, including warmer yellow/orange and cooler blue tints.
- Apple Support — Use Photographic Styles with your iPhone cameraSupports: that Photographic Styles adjust specific colors and let users change tone, color, and intensity.
- ColorNote photo guide for color analysisSupports: ColorNote’s first-party explanation of daylight, white balance, filters, sample location, and the boundary between web and app flows.
Sources and implementation last checked: .