Volume 21
March 2018 Edition
Law enforcement users of facial recognition technology must live by a clearly defined process and rock-solid policies that are rigorously exercised and audited .
• The goal of the peer review process is to showcase how you selected your candidate and came to your conclusions .
• Ultimately , you want to reasonably place the known candidate who was matched and selected from the facial recognition gallery search at the scene of the crime .
• To complete the second level verification , a majority rules voting system should take place .
• The question everyone should be asking is :
• “ Could this be the person we are looking for ?
• Could this reasonably be the suspect with all factors considered ?”
Critical to note is that once a candidate has been identified through facial recognition and validated through peer review , an arrest still CANNOT be made .
While this is obvious to most facial recognition users in law enforcement , it is still where unintentional mistakes can be made .
• While many agencies act in good faith to acquire expert testimony from facial recognition analysts , they sometimes unintentionally fall short by making arrests based on that analysis , and not based on sound implementation of the recommended two-level verification process .
• Expert analysis can inform the process , but it should not replace it .
Lower Quality Images Require More Vetting and Analysis
An enhanced approach should be taken with regard to images of lower quality .
• These images require more vetting and analysis .
• When surveillance video is described as “ grainy ,” the image lacks quality data because it is of lower resolution .
• This may cause an expert analyst to remain neutral .
• If that happens , there is no longer a need for a facial recognition comparison using algorithms .
• As a general rule of thumb , I always teach :
• “ If you can ’ t see a face , the facial recognition system can ’ t see a face .”
• “ If you are presented with a lower quality image that a human being cannot effectively analyze , do not perform any type of comparative facial analysis .”
• “ It will rarely be credible .”
I ’ ve seen many articles discuss the high probabilities of facial recognition false positives or the potential for high rates of misidentifications .
However , a key to law enforcement success with facial recognition is reducing reliance on facial recognition software alone or expert testimony alone .
• In police terminology , “ Call for back up !” Back up your “ faces ” with peer reviews and other supporting documents to validate any facial recognition match candidates as viable suspects .
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