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Deep Learning Solves Nuisance Alarms and Tailgating in Access Control

Access control systems are plagued by problems, such as false alarms and tailgating. Concepts of deep learning can reduce these problems.

Larry Anderson, Editor
SecurityInformed.com and SourceSecurity.com

Originally published on SecurityInformed.com

Access control systems are plagued by problems, such as false alarms and tailgating. Hakimo is a new company that applies concepts of deep learning to reduce false alarms and tailgating, and to make access control more accurate.

False or nuisance alarms from access control systems take a lot of time and attention for operators in global security operations centers (GSOCs). It’s wasted time they could use to perform more high-level duties. Hiring enough GSOC operators to monitor and resolve hundreds of nuisance alarms is prohibitively expensive. Fortunately, technology can perform the job.

Eliminating Nuisance Alarms

Eliminating nuisance alarms also makes operators more attentive to legitimate alarms, rather than disregarding them as background noise. There is much less chance that an actual alarm would get lost among the nuisance alarms.

In many cases, users ignore alarms and become conditioned to think that every alarm is a false alarm,” said Samuel Joseph, the Co-Founder and Chief Executive Officer (CEO) of Hakimo, adding “It’s like the boy who cried wolf.


Video And Data Analysis Using Deep Learning

To address the problem, Hakimo analyzes video and other data using deep learning, a form of artificial intelligence (AI) that employs a deep neural network, in order to emulate the operation of the human brain. Data passes through multiple non-linear layers for faster processing.

In the case of Hakimo, video of a door entrance or exit provides the data, and the system analyzes quickly to verify an alarm and/or to ensure that only one person passes through a door, after a card swipe or other form of authorization.

Read the full article on SecurityInformed.com

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