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New Face Recognition Data on Shoplifting Reveals Extent of Organized Retail Crime

An analysis of biometric data from FaceFirst, a leading retail facial recognition provider, revealed that repeat shoplifters are more organized and aggressive than previously known, striking the same retailer multiple times across several locations.

In a shoplifting recidivism study of biometric data spanning a six-month period, FaceFirst found that 60 percent of known shoplifters were detected entering at least two separate locations of the same retail chain, while 20 percent visited three or more locations.

“Unfortunately for retailers, shoplifters are incredibly loyal to their favorite brands,” said FaceFirst CEO Peter Trepp. “Until now, it has been nearly impossible to prove beyond anecdotal evidence how pervasive and strategic recidivism is.”

The study challenges statistics from the National Association of Shoplifting Prevention (NASP) indicating that the vast majority of shoplifting is not premeditated. However, the study appears to validate National Retail Federation (NRF) reports that indicate a broad trend toward rising organized retail crime (ORC), defined as the professional theft of retail merchandise with the intent to resell for financial gain. The National Retail Federation estimates that organized retail crime is a $30 billion problem for stores each year in the United States, and the average cost of a single shoplifting incident is $559. An estimated $9.6 billion of stolen merchandise is returned fraudulently, while a 2018 Loss Prevention Research Council (LPRC) study found evidence that other items are sold at flea markets.

“Our team is working to integrate bio-recognition into retail stores for two primary reasons,” said Dr. Read Hayes, University of Florida Research Scientist, and Director at the LPRC, which aims to assist loss prevention professionals through in-depth research and analysis on the latest technology. “Facial feature-matching helps managers more rapidly recognize known violent or property offenders, helping make employees and shoppers safer.”

Facial recognition is used by retailers to quickly identify repeat offenders and notify in-store loss prevention personnel. “Face recognition makes it possible to stop crimes before they start,” stated Trepp. “Our retail customers have seen a decrease in external shrink by one third or more after implementing facial recognition solutions in conjunction with their current loss prevention processes.”

Hayes also foresees future adoption among retailers for use cases that go beyond security: “Secondly, many shoppers indicate they’d like to be recognized at their favorite brick and mortar stores, just as they are at their preferred shopping websites.”


The study analyzed face recognition match alerts of subjects who had been identified as known shoplifters across 100 big box, pharmacy and grocery retail locations. The study focused on activity spanning December 1 2017 to May 31 2018.


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