Comparing two faces online is now a matter of seconds. Whether you are checking whether a selfie matches a government ID, verifying a returning customer, or running a quick similarity test, AI face comparison tools give you a numerical score and a match verdict with no software to install. Here is exactly how to do it.
Step 1: Choose your tool
For a free online face comparison, go to Quantilence Face Similarity. The demo is live — no account, no signup, no credit card. It uses the same model that powers the production API, so the accuracy figures are real.
Step 2: Prepare your photos
The quality of your photos directly affects the reliability of the comparison. These practices give the best results:
Do:
- Use clear, well-lit photos where the face is visible
- Frontal or near-frontal angles (within 30–35 degrees of straight-on)
- Photos where the face takes up at least 20% of the image
- Consistent lighting — avoid harsh shadows across the face
- JPEG or PNG format
Avoid:
- Sunglasses, masks, or heavy occlusion
- Extreme profile angles (looking sideways)
- Motion blur or out-of-focus images
- Very low resolution (below 100×100 pixels for the face region)
- Heavy filters or face-altering effects
Step 3: Upload the two photos
On the Quantilence face similarity demo:
- Click Upload Photo 1 and select the first image (e.g., the selfie)
- Click Upload Photo 2 and select the second image (e.g., the ID crop)
- Click Compare Faces
Results appear in under 200ms.
Step 4: Read the results
The demo returns three values:
Similarity score (0–100) A number representing how similar the two faces are. Higher scores mean more similar.
| Score range | Interpretation |
|---|---|
| 80–100 | Very strong match — almost certainly the same person |
| 65–79 | Strong match — likely the same person |
| 40–64 | Borderline — image quality may be affecting results |
| 0–39 | Low similarity — likely different people |
These are general guidelines. The actual threshold for a match/no-match decision depends on your acceptable false accept rate for your specific use case.
Match verdict A binary match or no-match verdict based on a calibrated threshold. This threshold is set to 0.01% FAR — meaning the model is configured to accept fewer than 1 false match per 10,000 genuine pairs.
Confidence How confident the model is in its verdict. Low confidence alongside a borderline score is a signal to flag for manual review rather than automated acceptance or rejection.
Understanding the accuracy
The Quantilence face similarity model achieves 99.4% TAR at 0.01% FAR on IJB-C — the IARPA Janus Benchmark C dataset, which contains 3,531 subjects across controlled and unconstrained (in-the-wild) conditions.
This benchmark is important because:
- It is harder than older benchmarks like LFW, which most modern models saturate at 99%+
- It includes pose variation, occlusion, and mixed image quality
- The FAR of 0.01% means 1 false match per 10,000 pairs, making it realistic for identity verification
For KYC use cases, your operational threshold may be tighter (0.001% FAR) or looser (0.1% FAR) depending on the regulatory requirement and whether there is a human-in-the-loop review step.
Common failure cases
Different cameras and lighting conditions Comparing a selfie taken in dim indoor light with a scan of a government ID taken in clinical lighting is inherently harder than matching two similar-quality photos. A borderline score under these conditions does not necessarily mean the photos show different people.
Age gap Comparing a photo taken 10 years ago with a recent selfie reduces accuracy. The face embedding captures current appearance, not identity-invariant features across decades.
Photo editing and filters Snapchat-style filters, skin-smoothing, or face-altering apps change the facial structure enough to reduce the similarity score, even between two photos of the same person.
Twins Identical twins genuinely have high facial similarity scores. Face comparison tools are not designed to distinguish between identical twins — no AI currently is, reliably.
Using face comparison in a KYC workflow
In a production identity verification flow, face comparison is typically used as the selfie-to-ID matching step:
- User submits a government-issued ID (passport, driving licence)
- OCR or document parsing extracts the photo from the ID
- User takes a selfie or completes a liveness check
- Face comparison API compares the ID photo against the selfie
- A similarity score above threshold proceeds to the next step; a low score flags for manual review
The face comparison result is one signal among several. A robust KYC pipeline also includes document authenticity checks, liveness detection, and a human review queue for edge cases.
For a deeper look at how face comparison fits into KYC, see Face Verification API vs Traditional KYC.
Try it now
The Quantilence face comparison demo is free, takes under 30 seconds, and uses the same model available via REST API for production KYC and identity verification. No account required.