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Facial recognition systems store biometric data using a unique mathematical pattern. As a result, it is one of the safest and most effective identification methods in biometric technology. These individuals may be anonymized and kept private to reduce the risk of unauthorized access. Liveness detection technology distinguishes face images from real users. This prevents the system from being tricked by photos of real users.

What is the facial recognition reliability score? Confidence scores, also known as

Similarity scores are very important for face detection and comparison systems. It gives feedback on how similar the two images are to each other. A higher confidence score indicates that the two images are more likely to be the same person. Confidence scores, therefore, use AI to predict whether a face is present in an image or matches a face in another image.

Confidence Score Threshold

Each prediction the AI ​​face recognition system makes has an associated score threshold that can be changed. In typical scenarios, most auto-matches are performed with very high percentages, such as 99% or greater confidence. Matches with low confidence scores can be used to determine close potential matches, which can then be further evaluated by human researchers.

The benefits of facial recognition systems for policing are evident: detection and prevention of crime.

  • Facial recognition is used when issuing identity documents and, most often, combined with other biometric technologies such as fingerprints (preventing ID fraud and identity theft).
  • Face match is used at border checks to compare the portrait on a digitized biometric passport with the holder’s face.