Within the last few years facial recognition has been gaining significant traction in the public and private sectors. The technology provides the ability to accurately verify and identify people. Facial recognition technology has served as the entry point for artificial intelligence implementation in physical security. However, there has been much hype and romanticism built around current products. The goal of this article is to hopefully clear the air and demystify facial recognition.

There are a number of factors to put into consideration when making the decision to implement facial recognition. First up is the need for regular maintenance. End users seem to believe that simply installing the solution out of the box is sufficient enough for current and future implementation needs. Many customers have high expectations for artificial intelligence in general and the SI firms installing these systems are not well versed enough in the subject to set them straight. With that said let me break down the practical steps needed for not only maintaining facial recognition systems but AI systems in general.

The artificial intelligence and machine learning process generally breaks down into the following steps. All of which are difficult and requires the help of experts and people with intermediate or advanced training:

Getting the facial data points to model for recognition

Feature and functional design and engineering

Modeling the data points and applying to machine learning algorithms

Deploying to access control and other security systems

Maintenance of the system

In general, there needs to be a database of occupancy faces to work from in order to alert the operator of a person of interest. The SI should be in the position to provide suggestions for database integrations. In fact, I would argue that the vendor or OEM providing the software and hardware products should help in this the regard. There is one such vendor that provides this as apart of it’s product and service offerings...Just sayin.

The growth of facial recognition technology has given rise to a number of companies claiming that their solution is the best in the market. Some criteria that systems integrators should look for are the number of facial data points used to recognize a face, the machine learning framework, cost, and any integration challenges that a system may have.

The nature of the site also comes into play as this will determine the design and functional need for on-premise processing. Other factors to consider include the performance on actual sites and integration with video management systems, access control systems and cameras.

Beyond the technological aspects, there are regulatory factors that need to be considered as well when installing a facial recognition or AI system.

About the author

Sheldon Gabriel is the founder and CTO of Ai-tronics Systems. Ai-tronics specializes in physical security and building control systems mobile and embedded systems development tools that gives developers the ability to build cohesive, secure AI-based IoT and IIoT systems and solutions.