Face Recognition API: What to Check Before You Buy

A face recognition API lets your app detect faces, compare two faces, or search a face against a database through a simple web call. Before you buy one, test it on your own users: accuracy at your chosen threshold, speed on mobile networks, handling of poor images, and how the vendor stores face data. Certifications help, but your proof-of-concept decides.

What a Face Recognition API Does: Detect, Compare and Search

In general, most face recognition APIs expose a small set of operations. Knowing which ones you need prevents a common mistake: buying an API that cannot do the job your use case requires.

Operation	What it returns	Typical use
Detect	Where faces are in an image, plus quality and pose	Checking that a selfie contains one usable face
Compare (verify)	A similarity score between two faces	Matching a selfie to an ID portrait at onboarding
Enrol	A stored face template linked to your user ID	Saving a reference for later logins or check-ins
Search (identify)	The closest matches from a stored collection	Finding duplicate accounts or recognising members

Verification and identification behave very differently at scale, and that difference deserves its own reading. Here, the key point is simpler: write the exact operations into your requirements. “Face recognition” on its own is too vague for a contract.

General Cloud Vision APIs vs KYC-Grade Face APIs

Many teams start with a big cloud provider, because they already have an account. That can work, but the offerings differ more than their names suggest.

• Some general vision APIs only detect faces. For example, Google’s Cloud Vision documentation describes face detection, with no feature to identify who a person is.
• Some restrict recognition features. In 2022, Microsoft moved identification and verification in its Face API behind a Limited Access application process under its Responsible AI Standard.
• Some offer matching but leave the rest to you. AWS Rekognition, for instance, provides face comparison and collection search, while liveness, document checks and onboarding flows sit elsewhere.

KYC-grade face APIs, by contrast, centre on identity checks. They usually bundle liveness detection, capture SDKs, document matching and audit logs, and vendors tune them for selfie-to-ID comparison rather than photo tagging. So the right choice depends on whether you need a building block or a complete verification step.

Evaluation Criteria for a Face Recognition API

Vendor brochures quote a single accuracy figure. However, a single figure tells you little about how the API will perform for your users. Evaluate these criteria instead.

Accuracy at Your Threshold

First, every API returns a score, and you choose the threshold that turns it into yes or no. So ask for false match and false non-match rates at the threshold you plan to use, not at the vendor’s favourite point. Independent benchmarks such as NIST’s face recognition evaluations are a useful filter, as our explainer on the NIST FRVT 1:1 test describes. Still, nothing replaces testing on your own images.

Latency on Real Networks

Next, measure the full round trip from a phone on a mobile network, not just the server processing time. Check where the vendor’s servers sit relative to your users, because distance adds delay. Then ask about rate limits and how the API behaves under peak load.

Image Quality Handling

In reality, selfies are often dark, blurry, tilted and taken on cheap cameras. So test how the API scores those images, and whether it returns quality warnings you can show to users. An API that silently scores bad images causes false rejects you cannot explain.

Accessories and Demographics

Also include glasses, headscarves, masks pulled down, beards and a wide age range in your test set. Then check performance across skin tones and genders. Differences between groups are a known issue in face recognition, so measure them on your population before launch.

API vs SDK: When You Need On-Device Capture

Put simply, an API receives an image and returns a result. However, it cannot see how anyone captured that image. That gap matters for any use case where fraud is possible.

	API only	API plus capture SDK
Who captures the image	Your app or the user’s upload	The vendor’s SDK inside your app
Liveness and anti-spoofing	Only if you add it separately	Usually built in
Protection against injected video	None, because the API only sees a file	Device and camera checks are possible
Quality guidance for users	You build it	Provided in the capture screen
Best fit	Back-office matching, low-risk flows	Onboarding, payments and other fraud-prone flows

For onboarding and payments, pair the API with an SDK or a separate liveness detection layer. Otherwise, a printed photo or a deepfake can score as a perfect match.

Security and Data Retention Questions for Vendors

Privacy regulators treat biometric templates as high-risk data, which makes the vendor’s handling part of your own compliance. Ask these questions in writing:

1. Where do you process and store images and templates, and can we choose the region?
2. Do you store raw images, or only templates, and for how long?
3. Do you use our customers’ images to train your models?
4. How do you encrypt data in transit and at rest?
5. Which security certifications do you hold, such as ISO 27001?
6. How do we delete a user’s data, and how fast does deletion take effect?
7. What uptime do you commit to, and what happens during an outage?

In the end, clear, contractual answers matter more than marketing pages. If a vendor cannot answer them, the risk moves onto your balance sheet.

Proof-of-Concept Test Plan for a Face Recognition API

Fortunately, a two- to four-week proof of concept settles most questions. Use this plan to keep it fair across vendors.

1. Build a test set. Collect consented selfie and ID pairs that reflect your users’ devices, lighting and demographics.
2. Add hard cases. Include old ID photos, twins or siblings, accessories and low-light images.
3. Add attacks. Include prints, screen replays and, if you test an SDK, an emulator and a virtual camera.
4. Fix the threshold. Choose your target false match rate, then compare vendors at that same point.
5. Measure the experience. Record latency from real phones, retry rates and quality warnings.
6. Review the paperwork. Score security answers, data terms, pricing and support alongside the technical results.

Finally, keep the test set. It becomes your regression suite when the vendor updates its model, which happens more often than most buyers expect. For a developer’s view of the basics, see our walkthrough of face recognition in Python.

How Verihubs Approaches Face Recognition APIs

Verihubs designed its face recognition for identity checks, with compare, enrol and search operations for onboarding, re-verification and duplicate detection. According to the product page, the matcher has taken part in NIST FRTE 1:1 and 1:N evaluations. Teams can add liveness detection and capture SDKs, so the API receives genuine captures rather than files of unknown origin. For the identification side, see our note on NIST 1:N face identification.

Frequently Asked Questions About Face Recognition APIs

What is a face recognition API?

A face recognition API is a web service that detects faces in images, compares two faces, or searches a face against a stored collection. Apps call it over the internet and receive a score or a list of matches.

How accurate are face recognition APIs?

Accuracy depends on the threshold, the image quality and the population. Ask for false match and false non-match rates at your own threshold, check independent benchmarks such as NIST evaluations, and test on your own users.

Should I use a face recognition API or an SDK?

Use an API alone for low-risk back-office matching. For onboarding and payments, add a capture SDK or liveness layer, because an API alone cannot tell a live capture from a fake.

Can I use a general cloud vision API for KYC?

Sometimes, but check the features first. Some general APIs only detect faces, and some restrict recognition features. KYC-grade face APIs usually include liveness, capture tools and audit logs.

How much does a face recognition API cost?

Most vendors charge per call, with volume tiers and sometimes minimum commitments. Compare total cost including liveness, SDK licences and support, not just the price per match.

Choose a Face Recognition API on Your Data, Not on a Brochure

Of course, every face recognition API looks accurate in a demo. Yet the differences show up on your users: their phones, their lighting, their ID photos and the fraudsters who target your flow. So the buying decision should rest on a fair test, run at your threshold, with attacks included.

Also look beyond the match score. Liveness, capture quality, data handling and support decide how the API behaves in production. The vendor that wins on all of those, measured on your data, is the one worth signing.

Planning a face recognition proof of concept? Talk to Verihubs about testing on your own users.