Face Detector is an API that processes images to identify human faces and provides bounding box coordinates for each detected face. It is designed for scenarios where automated face localization within images is required, such as image analysis or similar applications. The API accepts image uploads in .jpg, .jpeg, .png, .gif, and .webp formats, with a maximum file size of 400 KB for live testing.
This tool allows users to make POST requests to the /v1/facedetect endpoint, returning results in JSON, XML, or YAML formats. A live request feature is available directly from the documentation page, which does not require an API key for testing purposes. For integration into applications, users must obtain an API key and can refer to code examples provided for cURL, Node, and Python implementations. The API supports specifying a confidence parameter in the request payload.
Face Detector operates online and is accessible via the APIVerve platform. It offers a 99.9% uptime SLA and charges five credits per call. The authentication method for regular use is via an x-api-key header. Latency statistics are provided, with p50 latency at 1353 ms and p99 at 2379 ms, indicating typical response times. The service is positioned within the broader class of image analysis APIs.
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In the API design, testing & docs space, Face Detector takes a focused approach. Automatically detecting and locating human faces in images for security, tagging, or analysis applications. It is built as a B2B product for developers building image analysis or security tools. A free plan is available. The product ships for the command line and API.
It is developed by APIVerve, and the product first shipped in 2022. Key capabilities include face detection, bounding box output, and image analysis. It exposes integrations via a public API and an MCP server.
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