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

Amazonka.Rekognition.Types.Gender

Description

Synopsis

data Gender = Gender' {
- confidence :: Maybe Double
- value :: Maybe GenderType
}
newGender :: Gender
gender_confidence :: Lens' Gender (Maybe Double)
gender_value :: Lens' Gender (Maybe GenderType)

Documentation

data Gender Source #

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.

Constructors

Gender'
Fields confidence :: Maybe Double Level of confidence in the prediction. value :: Maybe GenderType The predicted gender of the face.

Instances

Instances details

FromJSON Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Methods parseJSON :: Value -> Parser Gender # parseJSONList :: Value -> Parser [Gender] #
Generic Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Associated Types type Rep Gender :: Type -> Type # Methods from :: Gender -> Rep Gender x # to :: Rep Gender x -> Gender #
Read Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Methods readsPrec :: Int -> ReadS Gender # readList :: ReadS [Gender] # readPrec :: ReadPrec Gender # readListPrec :: ReadPrec [Gender] #
Show Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Methods showsPrec :: Int -> Gender -> ShowS # show :: Gender -> String # showList :: [Gender] -> ShowS #
NFData Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Methods rnf :: Gender -> () #
Eq Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Methods (==) :: Gender -> Gender -> Bool # (/=) :: Gender -> Gender -> Bool #
Hashable Gender Source #
Instance details Defined in Amazonka.Rekognition.Types.Gender Methods hashWithSalt :: Int -> Gender -> Int # hash :: Gender -> Int #
type Rep Gender Source #
Instance details 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))))

newGender :: Gender Source #

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_confidence :: Lens' Gender (Maybe Double) Source #

Level of confidence in the prediction.

gender_value :: Lens' Gender (Maybe GenderType) Source #

The predicted gender of the face.