Security Dealer & Integrator

JAN 2017

Find news and information for the executive corporate security director, CSO, facility manager and assets protection manager on issues of policy, products, incidents, risk management, threat assessments and preparedness.

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January 2017 / Security Dealer & Integrator 21 features, a set of "vectors" is derived and contained in a 2 KB file. e primary FST use case is grabbing multiple images from a video feed and creating vectors from each face to compare against the database of possible vectors (N:N). is is computationally intensive, and it is very difficult; yet, for this in-motion application, FAR of <.03% and False Rejection Rate (FRR) of<.2% are achieved. e result of a false rejec- tion may just require another pass before the camera. One of the key elements that contributes to the system's reliabil- ity is an advanced artificial intelli- gence-based anti-spoofing algorithm, officially approved in the U.K., that prevents using any type of picture – color, B&W or IR – from spoof- ing the system. Furthermore, FST does not rely solely on the facial vector comparison to make their In Motion Identification system operate successfully. As I have written about recently, the use of artificial intelligence and deep learning is leading to smarter systems that learn as they go along. FST learns "body behaviors" extracted from the visual data it is constantly accumulating. Physical attributes and personal tendencies are accumulated into their own vec- tors that, along with the facial vector, form a user record. ese vectors work together in a flexible way to make a decision on a match. Biometric solutions that do not store personal information but can still conclusively identify a person – all without the vulnerability of being used by someone else to imitate that person – are the safest option from an identity threat standpoint," says Colleen Dunlap, CEO of Stone Lock Global. "Over time, we foresee that the advances in computing power and algorithms will make our decision process more dependent on the body behavior elements than facial," says Arie Melamed Yekel, FST's CMO. The Future of Facial In the future, facial recognition will evolve from identification and authentication to monitoring cer- tain types of behaviors. For exam- ple, General Motors has announced a system that will monitor a driver's face to determine if he/she is actually paying attention to the road while the vehicle is in cruise control. Imagine the possibilities as behavioral analytics evolves, where a system could differentiate what it has learned to be normal facial expressions from those that are abnormal. en factor in learned body behaviors. is "learning" is made possible by recent advances in processing power and the tools that allow those processors to learn over time as they encounter more data. Not only does this create exciting new possibilities for surveillance and public safety applications, business ramifications will also become apparent. ■ » Ray Coulombe is Founder and Managing Director of SecuritySpecifiers. com and He can be reached at ray@SecuritySpecifiers. com, or follow him on Twitter, @RayCoulombe. SECURITY THAT SCALES WHEN SECURING A COMMERCIAL PROPERTY, GO WITH AN INDUSTRY LEADER AND GO WIRELESS. Intrusion Alarms, Panic Alarms, Environmental Sensors • Simple and flexible integration • Single facility to campus coverage • Mission-critical reliability View our entire suite of wireless security products at: See what's new! Plan to visit us at ISC West, Booth #7065 It just works.

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