FaceFirst partners with Loss Prevention Foundation for biometric security and privacy training

The Loss Prevention Foundation (LPF) has partnered with FaceFirst to enhance its LPQualified and LPCertified certification programs for loss prevention leaders with biometric privacy and compliance training.

The new content is designed to ensure retailers can follow best practices for responsible biometrics use, including handling and purging data, human verification, encryption, face redaction and other key privacy concepts. The biometric content will be available mid-year.

A stream of lawsuits in Illinois, mostly relating to non-compliant data collection procedures, has grown to a torrent following a court decision that procedural violations constitute harm. The lawsuits and attendant publicity may have a chilling effect on the adoption of biometrics by retailers.

More than 170 loss prevention professionals representing more than 75 leading retail companies contributed to the LPQualified course and exam, and more than 300 people from over 130 organizations contributed to the LPCertified course and exam, according to the announcement. Proctored exams are offered at 1400 locations in the U.S. and 6000 worldwide.

“The new biometric security content is going to greatly enhance our certification,” comments Loss Prevention Foundation President Terry Sullivan, LPC. “Our partners are what make it possible for us to continue advancing the LP profession through education.”

FaceFirst will be the exclusive provider of biometric privacy content for LPF, and will also offer scholarships to qualified industry professionals under the partnership.

“The Loss Prevention Foundation is the most respected educator in the retail security space,” says Peter Trepp, FaceFirst CEO and author of The New Rules of Consumer Privacy. “Our partnership not only fulfills much-needed demand for industry best practices but is a sign that this transformative technology is here to stay.”

FaceFirst recently introduced Two-Minute Deployment which drastically reduces the time and effort necessary for enterprise face recognition platform installation by identifying networked cameras and automatically assigning them to servers.

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