About the API:
Using deep learning, this API runs an analysis of the face URLs that you pass to it. Be able to analyze the gender and estimated age of the person, and also, additional features.
This API will receive the image URL that you want to analyze. After running the scan, you will receive:
The number of faces detected.
The emotion that is expressed on the face.
Estimated Age.
Gender.
If the person has a mask or not.
If the person has the eyes closed or not.
Is the mouth open or not? Is the person smiling?
All this data will be delivered by the API.
This API is ideal to use alongside other face analysis ones. You will be able to predict someone's age and avoid the users from making further actions on your platforms.
Also, be able to determine the genders to take decisions based on that.
Detect multiple people in one picture, so if you ask your users for a picture of themselves and you receive a picture with several people, you can stop further actions.
Is ideal to check if the person has the eyes open or not. Be able to analyze an image and detect if the person is giving attention or sleeping.
Besides API call limitations per month, there is a rate limit of 1 API calls per second.
To use this endpoint you must indicate the URL of a face in the parameter.
Face Analyzer - Endpoint Features
| Object | Description |
|---|---|
url |
[Required] |
{"statusCode": 200, "body": {"faces": [{"boundingBox": {"topLeft": {"x": 0.29, "y": 0.33}, "bottomRight": {"x": 0.7, "y": 0.71}}, "facialFeatures": {"Gender": "Female", "Smile": false, "Eyeglasses": false, "Sunglasses": false, "AgeRange": {"Low": 23, "High": 29}, "Emotions": ["CALM"]}, "landmarks": {"eyeLeft": {"center": {"x": 0.38, "y": 0.47}, "left": {"x": 0.35, "y": 0.47}, "right": {"x": 0.42, "y": 0.47}, "down": {"x": 0.38, "y": 0.48}, "up": {"x": 0.38, "y": 0.47}}, "eyeRight": {"center": {"x": 0.57, "y": 0.46}, "left": {"x": 0.53, "y": 0.47}, "right": {"x": 0.61, "y": 0.46}, "down": {"x": 0.57, "y": 0.47}, "up": {"x": 0.57, "y": 0.46}}, "mouth": {"left": {"x": 0.41, "y": 0.61}, "right": {"x": 0.57, "y": 0.61}, "down": {"x": 0.48, "y": 0.63}, "up": {"x": 0.48, "y": 0.59}}, "nose": {"center": {"x": 0.47, "y": 0.54}, "left": {"x": 0.44, "y": 0.56}, "right": {"x": 0.51, "y": 0.56}}, "browLeft": {"left": {"x": 0.31, "y": 0.45}, "right": {"x": 0.41, "y": 0.43}, "up": {"x": 0.36, "y": 0.42}}, "browRight": {"left": {"x": 0.52, "y": 0.42}, "right": {"x": 0.65, "y": 0.43}, "up": {"x": 0.58, "y": 0.41}}, "chinBottom": {"x": 0.5, "y": 0.71}}}]}}
curl --location --request POST 'https://zylalabs.com/api/417/face+analysis+api/6826/face+analyzer?url=https://hips.hearstapps.com/hmg-prod.s3.amazonaws.com/images/tom-hardy-3-1537960058.jpg?crop=1xw:1xh;center,top&resize=480:' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Face Analysis API returns data related to facial analysis, including the number of faces detected, estimated age, gender, emotional expression, and specific features like whether the person is wearing a mask, has their eyes closed, or is smiling.
Key fields in the response data include "number_of_faces," "estimated_age," "gender," "emotion," "mask_status," "eyes_closed," "mouth_open," and "smiling." Each field provides specific insights about the detected faces.
The response data is structured in JSON format, with a main object containing status information and a body that includes detailed analysis results. For example, a successful response will include fields like "number_of_faces" and "emotion" within the body.
The Face Analyzer endpoint provides information on the number of faces detected, their estimated ages, genders, emotional expressions, and specific facial features such as mask-wearing and eye/mouth status.
The primary parameter for the Face Analyzer endpoint is the "image_url," which must be provided in the request. This URL should point to an image containing one or more faces for analysis.
Users can leverage the returned data to enhance user experience, such as filtering actions based on age or gender, ensuring user engagement by checking if faces are attentive, or managing content based on emotional expressions.
Data accuracy is maintained through advanced deep learning algorithms trained on diverse datasets. Continuous model updates and validation against real-world scenarios help ensure reliable analysis results.
Typical use cases include user verification in applications, enhancing user engagement by analyzing emotional responses, and filtering content based on demographic insights like age and gender.
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