Bad lighting, indirect angles, excess distance, poor camera quality, and low image resolution all will undermine reliability of matches. These poor image conditions are much more likely to occur when photos and videos are taken in public, such as with a CCTV camera. And these low-quality images taken in public often serve as face recognition probe images used in investigations.

Without regulations, law enforcement may employ irresponsible techniques that also exacerbate the risks of misidentification. Some agencies have engaged in the highly questionable practices of scanning police sketches of suspects in lieu of actual probe images of suspects or using computer editing to artificially fill in pieces of a face that were not caught on camera. Asking systems to analyze manufactured data will produce unreliable results. Computer programs do not “see” faces the way humans do, and artificially adding data that will be part of a face recognition scan is the equivalent to drawing in lines on a smudged fingerprint.

The reliability of face recognition also varies based upon the confidence threshold of potential matches. Confidence thresholds are a metric used to compare which proposed matches within a system are more likely to be accurate. The lower the confidence threshold, the more likely the “match” is actually a false positive. So, if law enforcement set face recognition systems to always return potential matches—no matter how low confidence the threshold—they will receive untrustworthy data. Yet some law enforcement entities do just that, including the FBI.

Law enforcement officials will sometimes dismiss misidentification risks by claiming face recognition is just used for leads. But using untrustworthy information as the foundation of an investigation is dangerous, regardless of whether that information is introduced in court. If law enforcement guidelines recommended basing investigations on contaminated DNA samples, it would be of little comfort that this tainted evidence was “just used for leads.” Simply being targeted in an investigation can be disturbing and disruptive and bring the prospect of being subject to harmful police action even if charges or a conviction never follow. And an individual could still be charged in part based on how a face recognition match impacts the early direction of an investigation. A technology with significant, known, and as-yet-unmitigated flaws should not be relied upon for investigative work.

One type of face recognition is especially likely to result in misidentifications: real-time scanning of crowds. Real-time face recognition systems do not attempt to identify a single probe image. Rather, these systems scan every person within a crowd that passes by the frame of a camera, and provide an alert if anyone scanned is identified as a match against a preexisting watchlist.

Real-time face recognition takes all the risks of using face recognition in an open-world setting—bad lighting, poor angles, excessive and inconsistent distances—and multiplies them by conducting scans of groups of individuals. Early results have shown the harm this could cause. In pilot programs in the United Kingdom, South Wales police had a 91% error rate and London Metropolitan Police had a 98% error rate.