Using similarity thresholds for associating and matching faces

Similarity thresholds are used for both associating and matching faces. What follows is guidance for using similarity thresholds for both use cases.

Using similarity thresholds for associating faces

When associating faces using the AssociateFaces operation, it’s important to ensure that faces being associated with a user are all from the same person. To help, the UserMatchThreshold parameter specifies the minimum user match confidence required for the new face to be associated with a UserID containing at least one FaceID already. This helps ensures that the FaceIds are associated with the right UserID. The value ranges from 0-100 and the default value is 75.

Using similarity thresholds to match faces

We allow you to control the results of all search operations (CompareFaces, SearchFaces, SearchFacesByImage, SearchUsers, SearchUsersByImage) by providing a similarity threshold as an input parameter.

FaceMatchThreshold, is the similarity threshold input attribute for SearchFaces and SearchFacesByImage, and it controls how many results are returned based on the similarity to the face being matched. The similarity threshold attribute for SearchUsers and SearchUsersByImage is UserMatchThreshold, and it controls how many results are returned based on the similarity to the user vector being matched. The threshold attribute is SimilarityThreshold for CompareFaces.

Responses with a Similarity response attribute value that's lower than the threshold aren't returned. This threshold is important to calibrate for your use case, because it can determine how many false positives are included in your match results. This controls the recall of your search results—the lower the threshold, the higher the recall.

All machine learning systems are probabilistic. You should use your judgment in setting the right similarity threshold, depending on your use case. For example, if you're looking to build a photos app to identify similar-looking family members, you might choose a lower threshold (such as 80%). On the other hand, for many law enforcement use cases, we recommend using a high threshold value of 99% or above to reduce accidental misidentification.

In addition to FaceMatchThreshold and UserMatchThreshold, you can use the Similarity response attribute as a means to reduce accidental misidentification. For instance, you can choose to use a low threshold (like 80%) to return more results. Then you can use the response attribute Similarity (percentage of similarity) to narrow the choice and filter for the right responses in your application. Again, using a higher similarity (such as 99% and above) reduces the risk of misidentification.