Pores API Response Scheme
Haut.AI provides an AI algorithm that estimates pores in different facial areas and the whole face.
Description
Skin usually has pores in different conditions. They contain tiny ostia from either pilosebaceous follicles (with sebaceous glands) or sweat glands that affect their condition. Enlarged, filamented, or black-headed pores require care. The algorithm calculates the size of each pore and the number of pores. The pores are classified into small and large pores. The ratio of skin area covered by large pores is non-linearly transformed to a score that is calculated for different facial areas and the whole face.
The higher the value of this parameter, the less large pores you have.
"algorithm_tech_name": "pores"
area_name
area_name
The algorithm returns metrics and sub-metrics for the following facial areas:
face
forehead
nose
right_cheek
left_cheek
main_metric
main_metric
The main metric is an overall score indicating the pores for each area_name
value
the pores score ranges from[0,100]
More large pores are associated with a lower value for this parameter"widget_type": "bad_good_line"
indicates that a higher value corresponds to a better skin condition. This score can be classified into six classes based onvalue
:(90,100]
- Excellent(80,90]
- Great(70,80]
- Good(50,70]
- Average(30, 50]
- Poor[0,30]
- Bad
"name": "Pores score"
"tech_name": "pores_score"
sub_metrics
sub_metrics
The algorithm returns sub-metrics for "area_name": face
only
Pores Number
estimates the number of detected poresvalue
The number of detected pores"widget_type": "numeric"
"name": "Pores Number"
"tech_name": "pores_number"
Pores Density
estimates the density of detected poresvalue
the density of detected pores on the skin"widget_type": "density"
"name": "Pores Density"
"tech_name": "pores_density"
masks_restored
masks_restored
The algorithm returns a vectorized mask of pores for a face-aligned image
"mask_type": "point_mask"
masks_original
masks_original
The algorithm returns a vectorized mask of pores for an original image
"mask_type": "point_mask"
Example (JSON)
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