# Choosing a similarity threshold

> Turn a 0 to 1 cosine similarity into accept, review and reject decisions using your own data, not a number copied from another vendor.

Source: https://sightradar.com/docs/guides/thresholds

Every match carries a `score`, cosine similarity between two faceprints from 0 to 1. Compare additionally returns `match`, evaluated at a default threshold. Neither is a verdict on its own. The right cut-off depends on your photos, your population, and the cost of each kind of mistake.

## Start from your own pairs

### Collect labelled pairs

A few hundred **same-person** pairs and a few hundred **different-person** pairs drawn from your real photos: same cameras, same lighting, same demographics. Event galleries, ID documents and webcam frames behave differently.

### Score them

Run each pair through `POST /v1/compare` (or index one side and search with the other). Record the score and the label.

### Plot the two distributions

Same-person scores cluster high, different-person scores cluster low, and there is an overlap region. Your threshold lives somewhere in that overlap. Moving it right trades false accepts for false rejects.

### Pick bands, not a point

For most products two thresholds beat one:

| Score                    | Decision             | Example UX                                    |
| ------------------------ | -------------------- | --------------------------------------------- |
| above the high threshold | accept automatically | show the photo in the guest's gallery         |
| between the two          | route to review      | "Is this you?" confirmation, or a human queue |
| below the low threshold  | reject               | hide                                          |

## Rules of thumb

* **Identity verification** (KYC, account recovery) should sit on the strict side and pair the face match with document authentication and liveness. A face match proves resemblance, not that the document is genuine or the person is live.
* **Photo discovery** (galleries, tagging) can sit looser, because a wrong suggestion is cheap and the user confirms it.
* **Search `threshold` and `limit`** are request parameters. Use them to cap what comes back, but keep the banding logic on your side where you can change it without redeploying prompts to users.

## Do not carry numbers across vendors

Rekognition reports similarity on a 0 to 100 scale; SightRadar uses 0 to 1. Dividing by 100 converts the scale but not the operating point: different models put the same decision at different cut-offs. Re-derive the threshold from your own pairs when you migrate, and compare providers on **ranking agreement**, not raw scores. The [Rekognition shim](/docs/sdks/rekognition-shim) converts the scale automatically so existing call sites keep working while you recalibrate.

## Watch it in production

Log the score and the decision, never the images. A drifting accept rate is the earliest sign that your photo mix has changed (a new venue, a new camera) and the threshold needs revisiting.

Further reading: [Choosing a face-match threshold](/blog/choosing-a-face-match-threshold) on the blog.