It is crucial manufacturers’ goals align with their end users’ needs when it comes to driving the development of embedded solutions with advanced sensors (e.g. cameras, microwaves, and LIDAR), operational analytics (facial and pattern recognition, tracking, and object discrimination), and active response (entry lockout, alert notification) which now prevent or deter risks detected at the entry.
“To this point, the security entrance must be part of the general building operations design, clearly separated from an architect’s complete authority. Most secured entries are specified in Division 28 [Electronic Safety and Security], outside of building design since it is structural and falls under code compliance surrounding emergency egress as well as building capacity and throughput. Therefore, if this is to work, the rules for design and the merging of Division 28 and Divisions 8 (Openings) and 11 (Equipment) must become refined, practical, and widely accepted,” says consultant Ben Butchko, CEO at a security solutions provider, and a former security engineer with ExxonMobil.
Security entrances often combine a number of systems, sensors, and requirements to achieve the goal of tailgating mitigation. When deployed, these entrances are inherently an integrated solution combining access control, mechanical hardware, sensors, algorithms, and, most importantly, design.
The addition of surveillance cameras around doors or security entrances can be an example of adding video deployment primarily for the sake of forensics: the ability to tie what took place at the entrance to an alarm condition (e.g. a forced or jammed/propped entrance/exit or a tailgating incident). This functionality can be enhanced by analytics. For example, facial recognition could be used to determine the individuals that set off the alarm condition. Analytics can also be proactive, determining a crowd has gathered, and then automatically activating additional security entrances, bringing them online and ready for credentials until the crowd has passed through.
Additionally, at this time there is an increasing demand for touchless access to support health and safety initiatives due to COVID-19.
“In this case, the integration of technologies and the use of machine learning can be leveraged to provide efficient, safe, and secure access. Machine learning and AI are well adapted to leveraging data sets and, over time, gaining an understanding of conditions and matching them to access control and individual requirements,” said Salvatore D’Agostino, CEO of a security systems provider.
Calculating the AI benefits
So, what functions can AI potentially add at a secured entrance? Currently, some of the best applications of AI are those that replace human effort at tasks that would be tough for people to do reliably and consistently, such as learn behaviors of staff, employees, and contractors and identify people and monitor them 24/7.
If a camera with AI is paired with a security entrance solution, such as a turnstile, security revolving door, or mantrap portal, and monitoring that entrance continuously, it could improve the detection capabilities currently built into these entrances in terms of identity verification and anti-tailgating/anti-piggybacking. Currently, security entrances detect tailgaters by using near-infrared sensors—an alarm is generated if two separate objects appear to break through the sensor beams. In security revolving doors and mantrap portals, near-infrared, ‘time of flight’ technology is paired with optics to create a 3D image of the objects inside the door, and algorithms and sampling data are used to determine whether there is one or more people.
False rejection happens when these technologies incorrectly reject a user (e.g. a person enters a door with a box of pizza and wears a backpack). Advanced AI can fill the gap by improving its models for recognizing people (through learned movement patterns and spacing of features) and objects, which can, in turn, decrease the false rejection rate. For example, it could ‘learn’ to know the difference between inanimate objects being worn or carried through the entrance versus living users.
Butchko says machine learning as well as deep learning has been used for many years in the big data world to identify trends and produce metrics regarding human intent. The use of “synthetic cognition” or AI is part of the drive for establishing ways to create the correlation to human patterns and the completion of tasks in the hopes of creating greater efficiencies in business practice.
“Within the security industry we see a trend by companies to leverage these specific engines to gain greater benefits from access control, VMS (video management system), intrusion detection, and tracking systems. It can be seen as a double-edged sword, because tracking learned behavior can help define potential vulnerabilities and unmask possible threats. It can also lead to privacy and discrimination concerns, especially when intent and analytic detail are not clear,” Butchko says.
