Redness API Response Scheme

Haut.AI provides AI algorithms that analyze the overall skin redness and skin irritation for different facial areas and for the whole face.

Description

Our skin has a natural pink color because of our blood vessels. Increased skin redness can be associated with allergic reactions and inflammatory processes. The most common environmental factors leading to facial redness are cold air or ultraviolet radiation.

A higher skin redness level is associated with a lower value for this parameter.

"algorithm_tech_name": "redness"

area_name

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

• face

• forehead

• nose

• right_cheek

• left_cheek

main_metric

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

"main_metric": {
                "value": 77,
                "widget_type": "bad_good_line",
                "name": "Redness Score",
                "tech_name": "redness_score",
                "widget_meta": null
            },
           

• value the redness score ranges from [0,100] A higher skin redness level is associated with a lower value for this parameter. The redness 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 is better, i.e., 100 corresponds to the absence of red areas on the skin while 0 corresponds to extremely irritated skin

• "name": "Redness score"

• "tech_name": "redness_score"

sub_metrics

The algorithm returns two types of sub-metrics - Redness Global Score and Redness Local Score

 "sub_metrics": [
                {
                    "value": 89,
                    "widget_type": "bad_good_line",
                    "name": "Redness Local Score",
                    "tech_name": "redness_local_score",
                    "widget_meta": null
                },
                {
                    "value": 64,
                    "widget_type": "bad_good_line",
                    "name": "Redness Global Score",
                    "tech_name": "redness_global_score",
                    "widget_meta": null
                }

• Redness Global Score evaluates the degree of global rednesses for eacharea_name

  • value the redness global score ranges from [0,100] A higher skin redness level is associated with a lower value for this parameter

  • "widget_type": "bad_good_line" indicates that a higher value is better

  • "name": "Redness Global Score"

  • "tech_name": "redness_global_score"

• Redness Local Score evaluates the degree of local rednesses (blood vessels, irritation, etc.) for each area_name

  • value the redness local score ranges from [0,100] A higher local redness level of the skin is associated with a lower value for this parameter

  • "widget_type": "bad_good_line" indicates that a higher value is better

  • "name": "Redness Local Score"

  • "tech_name": "redness_local_score"

masks_restored

The algorithm returns a vectorized heatmap mask of regions with high redness 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 redness for an original image

• "mask_type": "heatmap_mask"

• features

  • geometry

    • "type": "Multipolygon"

  See more on Masks here

Example (JSON)

10KB

redness.json