Age and Gender

Get the Age range and Gender of a person in an image

This particular endpoint is designed to offer you the ability to retrieve information about a person's age range and gender from an image.

To utilize this endpoint, you would need to provide an image containing a person's face . The endpoint would then analyze the image to determine the individual's age range and gender.

It is important to note that the accuracy of the results obtained from this endpoint may depend on various factors such as the quality of the image, lighting conditions, and other environmental factors.

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TEST DATA

image: 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

πŸ“˜

POST

{{URL}}/identitypass/verification/biometrics/face/age_and_gender

Request Header

KeyDescription
app-idYour App ID
x-api-keyYour Secret Key

Request Body

KeyDescription
imageface image URL(base64,png,jpeg)

Sample Response

{
    "status": true,
    "response_code": "00",
    "message": "Face Information Retrieved",
    "data": {
        "age": {
            "Low": 49,
            "High": 57
        },
        "gender": {
            "Value": "Male",
            "Confidence": 99.88365173339844
        }
    }
}

Response Description

ResponseDescription
statusIndicates that the request was successful.
response_codeRepresents the response code.
messageProvides additional information or a descriptive message related to the response.
dataAn array containing the information received.
ageProvides information about the estimated age of an individual. It includes two sub-fields:
LowRepresents the lower range of the estimated age.
HighRepresents the higher range of the estimated age.
genderProvides information about the gender of an individual. It includes two sub-fields:
ValueRepresents the predicted gender.
ConfidenceRepresents the confidence score or level of certainty associated with the gender prediction.