Install Jentic One Beta

Connect the Facial Recognition Reverse Image Face Search API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Facial Recognition Reverse Image Face Search API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Ffacecheck.id%2Ffacecheck" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Ffacecheck.id%2Ffacecheck" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Facial Recognition Reverse Image Face Search API.

Upload a probe face image and receive an upload identifier for downstream search

Run a reverse face search across the indexed open-web image corpus

Poll for a queued search result and retrieve ranked matches with confidence scores

Inspect upload metadata to confirm a photo was processed correctly before searching

GET STARTED

Start building with Facial Recognition Reverse Image Face Search API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Facial Recognition Reverse Image Face Search API for, with concrete tasks.

★ Romance Scam Detection

Investigate whether a profile photo from a dating or social platform appears on known scam, catfish, or stolen-image sites. The API uploads the probe image, runs a reverse search across the open-web index, and returns ranked URLs with confidence scores so a reviewer can decide whether the profile is genuine. Typical end-to-end latency is under a minute for a single probe.

Upload the suspected scam profile photo to /api/upload_pic, call /api/search with the returned id_search, poll until status is complete, and return the top five match URLs with their score values.

OSINT Person-of-Interest Lookup

Investigators with a single face photo and no name can search the open web for sources that contain the same face, surfacing social media profiles, news articles, and forum posts. The four-call flow covers upload, search, info, and delete, allowing a clean session that does not retain the probe image after the lookup. Suited to low-volume manual investigations rather than mass enrichment.

Upload the probe face image, run /api/search, collect the ranked URL list, and call /api/delete_pic to remove the uploaded photo when the search completes.

AI Agent Identity Triage

An AI agent fielding an investigative query receives a face image and needs to surface candidate sources for a human reviewer. Through Jentic, the agent calls the Jentic search index for 'reverse image face search', loads the FaceCheck operation schema, and executes the four-step upload-search-info-delete flow without holding the API key in its context. Useful when face search is one tool among many in a wider research workflow.

Call jentic.search with query 'reverse image face search', load the upload_pic and search operations, execute them in sequence with the supplied photo, and return ranked matches above a 70 score threshold.

Key Endpoints

4 endpoints — facecheck.

METHOD

PATH

DESCRIPTION

POST

/api/upload_pic

Upload a probe face image

POST

/api/search

Trigger a reverse face search and retrieve ranked matches

POST

/api/info

Get upload metadata for a probe image

POST

/api/delete_pic

Delete a previously uploaded probe image

POST

/api/upload_pic

Upload a probe face image

POST

/api/search

Trigger a reverse face search and retrieve ranked matches

POST

/api/info

Get upload metadata for a probe image

POST

/api/delete_pic

Delete a previously uploaded probe image

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring the FaceCheck.ID API by hand means placing its token in the Authorization header, targeting the facecheck.id host, and sequencing the upload-then-search calls yourself. Through Jentic you install once, import the FaceCheck.ID API from the API Directory, store the token once, and your agent calls it.

Permission scoping

FaceCheck.ID carries the image reference in the request body rather than a resource id in the path, so you limit the agent to the operations it needs, such as uploading a picture and running a search. You choose those operations, so the delete-picture call is not included unless you add it.

Credential isolation

Your FaceCheck.ID token is stored once, encrypted, by your own Jentic One instance and injected as the Authorization header at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'reverse image face search', and Jentic returns the FaceCheck operations with their input schemas so the agent calls upload_pic and search in order without browsing the facecheck.id docs.

FAQs

Specific to using Facial Recognition Reverse Image Face Search API through Jentic.

What authentication does the FaceCheck.ID API use?

FaceCheck.ID uses an API token passed in the Authorization header under the Bearer apiKey scheme. Through Jentic, the token is stored encrypted in the vault and never enters the agent's context - the agent receives a scoped execution permission instead of the raw key.

Can I run a reverse face search with the FaceCheck.ID API?

Yes. The flow is to POST a probe image to /api/upload_pic, call /api/search with the returned id_search to queue the lookup, and poll the same endpoint until the search completes and ranked URLs with score values are returned.

What are the rate limits for the FaceCheck.ID API?

The OpenAPI spec does not declare numeric rate limits. FaceCheck enforces credit-based pricing per search rather than per-second quotas, so plan limits scale with the credits attached to the API token.

How do I run a face search with the FaceCheck.ID API through Jentic?

Run pip install jentic, then call jentic.search with the query 'reverse image face search'. Load the upload_pic operation schema, execute it with the probe image, then load and execute the /api/search operation with the returned id_search to retrieve the ranked match list.

How do I remove an uploaded photo from FaceCheck.ID?

Call POST /api/delete_pic with the id_search returned by the upload step. This removes the probe image from the queue and is the recommended cleanup step at the end of an investigation session.

Is the FaceCheck.ID API free?

FaceCheck.ID operates on a paid credit model - searches consume credits attached to the API token. A limited free tier is available for testing the upload and search flow. Get started with Jentic One, the self-hosted execution layer.

Can I limit what my agent is allowed to do with the FaceCheck.ID API?

Yes. Because you run Jentic One yourself, your own rules decide which FaceCheck.ID operations the agent may call and which credentials it may use. FaceCheck.ID carries the probe image in the request body rather than a resource id in the path, so you can grant only the operations a task needs, such as upload_pic and search, while leaving out info or delete_pic. The token is injected at execution time and never enters the agent's context, so the agent can only run the operations you allow.

Facecheck Id Facial Recognition Reverse Image Face Search API

AI ReadinessNon-Ready (F)27/100

See full scorecard

Official vendor OpenAPI document · agent-readyIdentity AuthIdentity VerificationapiKey4 EndpointsREST

For Agents

Search the open web for matching faces from a probe photo and retrieve ranked match URLs with confidence scores. Useful for identity verification, scam detection, and OSINT lookups.

Use for: I need to find where else a person's photo appears online, Search for matching faces from a probe image, Check whether a profile picture appears on scam or fraud sites, Find all public web pages containing this face

Not supported: Does not handle document verification, liveness checks, or KYC compliance - use for reverse face search across publicly indexed images only.

FaceCheck.ID is a reverse image face search API that lets applications upload a photo and find matching faces across publicly indexed sources on the open web. The service is built around four operations: upload a probe image, trigger a search, poll for the result, and delete the uploaded photo when finished. It is commonly used in OSINT investigations, scam detection, dating-site verification, and person-of-interest workflows where a face image is the only available identifier.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

100

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A+Signals: 4

99%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

100%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: C+Signals: 4

11%

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

25%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: FSignals: 4

13%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

25%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C-Signals: 1

50%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: D-Signals: 1

41%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Facial Recognition Reverse Image Face Search API to your agent

Capabilities

Start building with Facial Recognition Reverse Image Face Search API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Facecheck Id Facial Recognition Reverse Image Face Search API

Jentic AI Readiness Score

Facial Recognition Reverse Image Face Search API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness