The FaceMatch Recognition API delivers a straightforward, high-performance service for assessing facial similarity between two photographs. Send two image files (image1 and image2) in a single multipart/form-data POST request; receive an immediate JSON response with a boolean success flag and a human-readable similarity score (e.g. "92,7%").
{
"success": true,
"similarity": "83,4%"
}
curl --location --request POST 'https://zylalabs.com/api/9242/facematch+recognition+api/16691/compare' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"url1": "https://i.imgur.com/cLq8fWd.jpeg",
"url2": "https://i.imgur.com/8hUOMbU.jpeg"
}'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
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The API returns a JSON response containing a success flag and a similarity score. The success flag indicates whether the comparison was successful, while the similarity score represents the percentage of likeness between the two faces compared.
The key fields in the response data are "success," which is a boolean indicating the operation's success, and "similarity," which is a string representing the percentage of similarity between the two images (e.g., "83,4%").
The response data is structured in JSON format. It includes two main fields: "success" (boolean) and "similarity" (string). This organization allows for easy parsing and integration into applications.
The compare endpoint accepts two parameters: image1 and image2, which should be provided as image files in a multipart/form-data POST request. Users must ensure that both images are included for a valid comparison.
Typical use cases include identity verification, security applications, social media tagging, and photo organization. The API can help determine if two images depict the same person, enhancing user experience in various applications.
Data accuracy is maintained through advanced facial recognition algorithms that analyze key facial features. Continuous updates and improvements to the underlying technology ensure high-quality results in face comparisons.
Users can utilize the returned data by checking the "success" field to confirm if the comparison was valid and using the "similarity" score to assess how closely the two images match, which can inform decisions in applications like security or user verification.
If the API returns a "success" value of false, users should check for potential issues with the input images, such as unsupported formats or insufficient quality. Implementing error handling in the application can help manage these scenarios effectively.
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