The AWS Rekognition study I believe you are referring to was fundamentally flawed in two ways.
First, the threshold used was the default suggested for one-to-one matching (lower false reject rate, higher false accept rate). It doesn't make sense to use this threshold for search applications, and when the study was reproduced with a more appropriate (higher) threshold there were 0 mis-identifications.
Second, law enforcement use of face recognition doesn't even involve the algorithm making a lights-out identification decision. Instead, the most similar faces are presented to the user in ranked order by similarity (like a search engine). It's a tool for generating investigative leads, often preferable to publishing the face image of a wanted perpetrator on local news.
First, the threshold used was the default suggested for one-to-one matching (lower false reject rate, higher false accept rate). It doesn't make sense to use this threshold for search applications, and when the study was reproduced with a more appropriate (higher) threshold there were 0 mis-identifications.
Second, law enforcement use of face recognition doesn't even involve the algorithm making a lights-out identification decision. Instead, the most similar faces are presented to the user in ranked order by similarity (like a search engine). It's a tool for generating investigative leads, often preferable to publishing the face image of a wanted perpetrator on local news.