The Face Comparison API uses Artificial Intelligence to compare two different pictures and determine if they belong to the same person.

The API requires the publicly accessible image URLs of the two pictures you want to compare.

The API provides an intuitive output in the form of a result message and a similarity percentage.

The result message will indicate whether the two faces belong to different people or the same person.

Yes, the input pictures need to be clean, with a visible and clear face of the person to compare. Blurry images, multiple people in the same picture, and unrecognizable faces may lead to comparison errors.

No, the API only accepts publicly accessible image URLs as input.

Each endpoint returns data related to face comparison results, including whether the faces belong to the same person, a confidence score, and image URLs used in the comparison.

Key fields include "is_same" (true/false), "confidence_score" (0 to 1), and "success" (indicating if the request was processed correctly). Additional fields may include image URLs and result messages.

The response data is structured in JSON format, with a top-level object containing "output" or "data" fields, which encapsulate the results of the face comparison and relevant metadata.

Each endpoint provides information on whether the two images depict the same person, the confidence level of the comparison, and additional metadata such as image URLs and result messages.

Users can customize requests by providing different publicly accessible image URLs for comparison. The API processes these URLs to return tailored results based on the provided images.

"is_same" indicates if the faces are from the same person, "confidence_score" reflects the reliability of the comparison, and "success" shows if the API call was successful.

Data accuracy is maintained through advanced AI algorithms that analyze facial features. Continuous training on diverse datasets helps improve the model's performance and reliability.

Typical use cases include identity verification for security systems, comparing images for fraud detection, and organizing large photo databases by individual identity.

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