Hirak FaceAPI

Name: Hirak FaceAPI API
Brand: Hirak FaceAPI
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLVisionapiKey5 EndpointsREST

For Agents

Detect faces, find facial landmarks, compare faces, analyse age, gender, and emotion, and generate face encodings via five POST endpoints. Authenticated with an X-RapidAPI-Key header.

Quickstart

Get started with Hirak FaceAPI in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"compare two faces in an image"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Hirak FaceAPI API.

Detect faces in an image and return bounding boxes from POST /detect

Locate facial landmarks such as eyes, nose, and mouth using POST /landmarks

Compare two faces for similarity to support verification flows via POST /compare

Analyse facial attributes including age, gender, and emotion through POST /analyze

GET STARTED

Start building with Hirak FaceAPI API

Explore with Jentic

View OpenAPI Document

Use for: I need to detect all faces in this image, I want to compare two photos to see if they match, Get the predicted age and emotion for a face, Generate a face encoding to compare against my database

Not supported: Does not handle full identity verification, document parsing, or biometric template storage — use for face detection, comparison, and attribute analysis only.

Jentic publishes the only available OpenAPI document for Hirak FaceAPI, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Hirak FaceAPI, keeping it validated and agent-ready. Hirak FaceAPI is a RapidAPI-distributed face analysis service offering five POST endpoints that detect faces, locate landmarks, compare two faces, analyse facial attributes such as age, gender, and emotion, and produce face encodings suitable for downstream matching. It is authenticated with the X-RapidAPI-Key header and aimed at developers who need lightweight face features without the complexity of cloud-vendor vision SDKs. Image input is supplied as part of each POST request.

Use Cases

Patterns agents use Hirak FaceAPI API for, with concrete tasks.

★ Lightweight Identity Verification

Onboarding flows that need a 'selfie matches ID photo' check use POST /compare to receive a similarity score. The result lets an agent route the user to a manual review queue or auto-approve based on a threshold, without the integration weight of a full KYC vendor.

Given a selfie image and an ID photo, call POST /compare and route to manual review if the similarity score is below 0.85.

Photo Tagging in a Media Library

A media library tags photos with faces and emotions for search and discovery. Hirak FaceAPI's /detect, /landmarks, and /analyze endpoints return the structured data needed to populate per-photo metadata so users can filter by 'photos with smiling people'.

For each new uploaded photo, call POST /detect and POST /analyze, then store the bounding boxes and dominant emotion as photo metadata.

Content Moderation Pre-Filter

Moderation pipelines use face presence as a signal before sending content to a human reviewer. /detect quickly identifies whether an image contains a face, and /analyze can flag minors based on age estimates, letting the platform prioritise the review queue without scaling a full moderation team.

When an image is uploaded to the platform, call POST /analyze and queue any image where the estimated age is below 18 for human review.

AI Agent Face Matching Tool

An AI agent matches a candidate photo against a known set of encodings. Through Jentic, the agent searches for the encode operation, loads the schema, and executes POST /encode for each image, then performs vector comparison locally. The X-RapidAPI-Key stays in the Jentic vault.

For a new photo, search Jentic for 'generate face encoding', execute POST /encode, and compare the returned vector against the stored encodings to find the closest match.

Key Endpoints

5 endpoints — jentic publishes the only available openapi specification for hirak faceapi, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/detect

Detect faces in an image

POST

/landmarks

Locate facial landmarks

POST

/compare

Compare two faces for similarity

POST

/analyze

Analyse age, gender, and emotion

POST

/encode

Generate a face encoding vector

POST

/detect

Detect faces in an image

POST

/landmarks

Locate facial landmarks

POST

/compare

Compare two faces for similarity

POST

/analyze

Analyse age, gender, and emotion

POST

/encode

Generate a face encoding vector

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Your X-RapidAPI-Key is stored encrypted in the Jentic vault (MAXsystem). The agent receives a scoped execution token, and Jentic injects the header at call time so the raw key never enters the model context.

Intent-based discovery

Agents search by intent (e.g., 'compare two faces') and Jentic returns the matching POST /compare or /detect operation with its body schema, so the agent picks the right endpoint without reading docs.

Time to first call

Direct Hirak FaceAPI integration: half a day to wire image upload and the five endpoints. Through Jentic: under 30 minutes — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

FaceCheck.ID API

Reverse face search service for matching a photo against a public web index.

Choose FaceCheck.ID when the user wants reverse face search across the web; use Hirak FaceAPI for in-app detection, comparison, and analysis.

Complementary

OpenAI API

GPT-4 vision can describe faces in natural language to complement Hirak's structured outputs.

Use OpenAI vision when the agent needs a natural-language description; use Hirak FaceAPI for structured bounding boxes and similarity scores.

Complementary

Mindee API

Document understanding API for ID parsing alongside Hirak's face matching.

Pair Mindee with Hirak FaceAPI when an ID verification flow needs both document parsing and face match against the ID photo.

FAQs

Specific to using Hirak FaceAPI API through Jentic.

Why is there no official OpenAPI spec for Hirak FaceAPI?

Hirak does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Hirak FaceAPI via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Hirak FaceAPI use?

Hirak FaceAPI is distributed via RapidAPI and authenticated with the X-RapidAPI-Key header. Through Jentic, the key is held in the MAXsystem vault and injected at execution time so the raw RapidAPI key never enters the agent context.

Can I compare two faces for similarity?

Yes. POST /compare accepts two images and returns a similarity score that can be thresholded to decide if they are the same person. Combine with POST /detect first if you need to confirm both inputs contain a face before comparison.

What are the rate limits for the Hirak FaceAPI?

RapidAPI plan tiers govern throughput; the OpenAPI spec does not declare specific limits. Check your RapidAPI subscription for monthly quotas and queue large image-batch jobs to stay within plan limits.

How do I run face detection through Jentic?

Run pip install jentic, then await client.search('detect faces in an image'), load the POST /detect schema, and execute with the image payload. The response contains bounding boxes for any faces found.

Does Hirak FaceAPI store the face encodings I generate?

No. POST /encode returns a vector to the caller; storage is the integrator's responsibility. Persist encodings in your own vector store and call /encode again on each new candidate image to get a comparable vector.