Uniformness

Haut.AI provides AI algorithms that analyze the skin uniformness of different facial areas and the whole face.

Description

The uniformness metric shows how smooth and even your skin is. It takes into account eruption, age spots, freckles, and blood vessels close to the surface, along with texture-associated skin features. The higher the value of this parameter, the more uniform and smooth your skin is.

"algorithm_tech_name": "uniformness"

area_name

The algorithm returns metrics and sub-metrics for the following facial areas:

  • face

  • forehead

  • right_cheek

  • left_cheek

main_metric

The main metric is an overall score that indicates the uniformness of each area_name

"main_metric": {
                "value": 30,
                "widget_type": "bad_good_line",
                "name": "Uniformness Score",
                "tech_name": "uniformness_score",
                "widget_meta": null
            },
           
  • value The uniformness score ranges from [0,100]The higher the value of this parameter, the more uniform and smooth your skin is. The uniformness can be classified into six classes based on value:

    • (90,100] - Excellent

    • (80,90] - Great

    • (70,80] - Good

    • (50,70] - Average

    • (30, 50] - Poor

    • [0,30] - Bad

  • "widget_type": "bad_good_line" indicates that a higher value corresponds to a better skin condition

  • "name": "Uniformness score"

  • "tech_name": "uniformness_score"

sub_metrics

The algorithm doesn't return any

masks_restored

The algorithm returns a vectorized heatmap mask of regions with high non-uniformness for a face-aligned image.

  • "mask_type": "heatmap_mask"

  • features

    • geometry

      • "type": "Multipolygon"

    See more on Masks here

masks_original

The algorithm returns a vectorized heatmap mask of regions with high non-uniformness for an original image

  • "mask_type": "heatmap_mask"

  • features

    • geometry

      • "type": "Multipolygon"

    See more on Masks here

Example (JSON)

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