Face Detector API

About the Face Detector API

Face Detector API uses advanced computer vision algorithms to identify human faces in images. It returns the precise coordinates (x, y, width, height) and confidence scores for each detected face, making it ideal for applications like photo organization, face cropping, presence detection, and privacy masking.

What people use it for

Smart Avatar Cropping

Profile editors center user avatars around the detected face bounding box coordinates instead of cutting off heads with fixed center crops.

Privacy Photo Blurring

Blur sensitive facial areas automatically across community upload galleries before photos publish to public forums.

KYC Document Pre-Check

When applicants submit identity documents, check that exactly one face is present before triggering costly third-party identity checks.

Stock Library Categorization

Digital asset managers scan bulk photo collections to tag portraits based on face counts and filter out group shots automatically.

Ways to call it

One endpoint, many ways in — REST with JSON, XML, YAML and CSV, plus GraphQL and an MCP interface for AI agents.

JSON

Default REST response

XML

Markup format

YAML

Human-readable

CSV

Tabular export

Beta

GraphQL

Query language

New

MCP

For AI agents

Request parameters — POST /v1/facedetect

Sent as JSON in the request body. Premium parameters are accepted on every plan but only take effect on plans that include them. Anything not listed here is dropped rather than passed through.

Required

Optional

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curl -X POST "https://api.apiverve.com/v1/facedetect" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"confidence":0.5}'

Authentication

Send your key in the x-api-key header. That is the only auth step — no token exchange, and no per-endpoint scope to configure.

Response — POST /v1/facedetect

Every APIVerve endpoint returns the same three top-level keys, so one response handler covers your whole integration: status, error and data. Only data changes shape.

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{
  "status": "ok",
  "error": null,
  "data": {
    "faces": [
      {
        "x": 142,
        "y": 85,
        "width": 98,
        "height": 112,
        "confidence": 0.9847
      },
      {
        "x": 312,
        "y": 92,
        "width": 87,
        "height": 103,
        "confidence": 0.9623
      },
      {
        "x": 478,
        "y": 78,
        "width": 95,
        "height": 118,
        "confidence": 0.9412
      }
    ],
    "faceCount": 3,
    "hasFaces": true,
    "imageWidth": 640,
    "imageHeight": 480,
    "averageConfidence": 0.9627,
    "imageCoverage": 10.23
  },
  "code": 200
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Response headers

Every response carries your balance, so your own code always knows where it stands without polling anything. All four are exposed to browsers through CORS.

Errors

Read the HTTP status first, then error for the specific reason. The body names the parameter that has to change.

What a call costs — 10 credits per call

Credits are shared across every APIVerve API — one balance, one key, one bill. Failed requests never count against it, so a rejected input costs nothing.

Calls are what the credits buy at this API's rate — spend them here, on any of the other APIs, or across both. Nothing is reserved per endpoint.

Zero-code embed — one snippet, no backend

Drop an interactive Face Detector form onto any page. The widget calls the API for you, so no key ever appears in your markup and there is nothing to deploy.

Changelog — Face Detector API

Every change to this endpoint, newest first. Breaking changes ship as a new version and the previous one keeps serving — response fields are added, never removed or retyped in place, so an integration written against v1 keeps working.

Currently v1