Fingerprint performs browser identification, which is by nature probability-based. The confidence score reflects the system's degree of certainty that the visitor identifier is correct.
The confidence score is a floating-point number between
1 that represents the probability of accurate identification.
The closer this number is to
1, the more sure we are that the
visitorID is correct. The closer it is to
0, the more uncertainty we have about the identification results.
The confidence score is a statistical estimation of how often we were correct in cases similar to the one in question. For instance, a confidence score of
0.97 means that 97% of similar requests were identified correctly. New Fingerprint users do not undergo any calibration or stabilization period and will receive an accurate confidence score immediately.
Both the Fingerprint Pro and open source agent work by collecting multiple device/browser signals to create a unique device fingerprint. In the case of FingerprintJS Pro, the agent sends the signals to the API for backend processing; in response, the API returns the
visitorID along with the confidence score (i.e., the probability of the identification being accurate). In contrast, the open source agent calculates the confidence score based on the current browser name. Since this happens strictly on the client side, results are less accurate than with the Fingerprint Pro version.
A common use case of the confidence score is for setting thresholds — for example, if the result is below a certain threshold, 2FA may be invoked for the visitor in question. Alternatively, a captcha or additional challenge can be presented to the visitor in this case.
Updated about 1 month ago