For the complete documentation index, see llms.txt. This page is also available as Markdown.

Uniformness

What the Uniformness parameter measures about how evenly skin colour is distributed, and how to read its scores, grades, and mask.

About Parameter

The parameter measures how evenly skin color is distributed across the face. An uneven appearance can result from various factors, including patches of darker or lighter pigmentation, redness from inflammation, visible small blood vessels, and surface texture differences. When skin color is more uniform, the overall appearance looks smoother and more balanced.

Features

  1. Multi-area analysis: forehead, right cheek, left cheek.

  2. Works with 1 and 3 side facial images.

Output

  1. Score from 1 to 100.

  2. Grade from 1 to 5.

  3. Algorithm-assigned skin condition tag (Great, Good, Average, Poor).

  4. Score for specific facial regions: forehead, left cheek, right cheek.

  5. Vector mask.

Scores & Grades Description

Score value
Grade value
Description
Tags

100-90

1

Flawlessly smooth texture with invisible pores and completely consistent pigmentation.

Great

89-80

2

Only very faint grain or slight color variation visible upon close inspection.

Good

50-79

3

Noticeable pores, mild roughness, or uneven pigmentation are clearly visible.

Average

30-49

4

Significant irregularities.

Poor

<30

5

Extreme roughness, severe discoloration create a highly uneven, textured landscape.

Poor

Mask Color Legend

The uniformness mask shades color unevenness across the face. With the default color scale, lighter areas mark an even skin tone, and deeper indigo marks a more uneven skin tone.

Uniformness mask color scale, from light blue (even skin tone) on the left to deep indigo (uneven skin tone) on the right
Default color scale for the uniformness mask.

Algorithm API

See Uniformness Parameter Result Scheme.


Book a demo external-linkFace Skin Analysis 3.0 on haut.ai

Last updated