Copyright | (c) 2013-2023 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | Safe-Inferred |
Language | Haskell2010 |
Synopsis
- data Gender = Gender' {}
- newGender :: Gender
- gender_confidence :: Lens' Gender (Maybe Double)
- gender_value :: Lens' Gender (Maybe GenderType)
Documentation
The predicted gender of a detected face.
Amazon Rekognition makes gender binary (male/female) predictions based on the physical appearance of a face in a particular image. This kind of prediction is not designed to categorize a person’s gender identity, and you shouldn't use Amazon Rekognition to make such a determination. For example, a male actor wearing a long-haired wig and earrings for a role might be predicted as female.
Using Amazon Rekognition to make gender binary predictions is best suited for use cases where aggregate gender distribution statistics need to be analyzed without identifying specific users. For example, the percentage of female users compared to male users on a social media platform.
We don't recommend using gender binary predictions to make decisions that impact an individual's rights, privacy, or access to services.
See: newGender
smart constructor.
Gender' | |
|
Instances
FromJSON Gender Source # | |
Generic Gender Source # | |
Read Gender Source # | |
Show Gender Source # | |
NFData Gender Source # | |
Defined in Amazonka.Rekognition.Types.Gender | |
Eq Gender Source # | |
Hashable Gender Source # | |
Defined in Amazonka.Rekognition.Types.Gender | |
type Rep Gender Source # | |
Defined in Amazonka.Rekognition.Types.Gender type Rep Gender = D1 ('MetaData "Gender" "Amazonka.Rekognition.Types.Gender" "amazonka-rekognition-2.0-EaCrS9R3rWADqefEZvOx5B" 'False) (C1 ('MetaCons "Gender'" 'PrefixI 'True) (S1 ('MetaSel ('Just "confidence") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :*: S1 ('MetaSel ('Just "value") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe GenderType)))) |
Create a value of Gender
with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:confidence:Gender'
, gender_confidence
- Level of confidence in the prediction.
$sel:value:Gender'
, gender_value
- The predicted gender of the face.
gender_value :: Lens' Gender (Maybe GenderType) Source #
The predicted gender of the face.