INDUSTRY PROFESSIONAL

High Quality Integration

• By Eddie Reynolds
• Oct 06, 2020

Whether you are walking through U.S. customs at an airport or the lobby at a critical facility like a data center, a visitor checkpoint to verify your identity is standard. In fact, visitor management is an essential facet of the overall security solution, keeping employees, guests and assets safe from threats.

While there are several choices for visitor management platforms, biometric technologies have taken off over the last few years, most specifically facial recognition which is expected to be a $7 billion market by 2024. When compared to fingerprint or retina scanners, facial recognition is considered a less intrusive and more reliable way of tracking foot traffic for facilities for visitor management.

Face detection allows enterprises to adopt a more seamless and secure approach to visitor management, when compared to solutions that solely rely on access cards and codes—which are more easily manipulated. Whether an end user’s goal is to recognize, identify, or verify a person, visitor management solutions—such as those that use video surveillance systems equipped with facial recognition software and integrate with access control—can streamline visitor entry and allow enterprises to create a “virtual perimeter.”

However, in order for these solutions to be effective, the underlying video surveillance system must be set up to capture video optimally. And if your surveillance deployment isn’t paired with high-quality illumination, it will be difficult to guarantee actionable analytics results.

OPTIMIZING PERFORMANCE

Put simply, facial recognition video systems work by making use of security cameras to scan images, using algorithms to map faces and detect the features that make them unique. The system then translates this information into hundreds of data points, representing the geometry of one’s face.

These digital “face prints” are then used as reference points when employees or visitors arrive at a facility, comparing those who enter against a vast library. If the person matches the record of an approved employee or visitor, they will be granted access to the facility. Similarly, organizations can also use facial recognition to blacklist individuals, barring them entrance or keeping track of their presence and movements. However, the insights derived from facial recognition analytics are only as good as the images the system analyzes. Without clear, high-definition video, these solutions can often fall flat.

Studies, some dating back to the late 1990s, have shown time and time again that lighting plays a crucial role in the accuracy of facial recognition systems. In fact, it has been argued that changes in lighting conditions can make two images of the same person seem less similar than two images of different people, according to the National Institute of Standards and Technology in their research and findings outlined in the report, Quantifying How Lighting and Focus Affect Face Recognition Performance. Variances in brightness and direction of lighting in reference images can seriously hinder the detection capabilities and accuracy of facial recognition.

DEPLOYING A SYSTEM

For example, a large corporate campus might be deploying a video system with facial recognition software, the reference images taken of each employee or visitor will most likely be done in a well-lit space. While this yields a clear and accurate reference point, these same conditions may not always be present when the solution is in use. If the surveillance cameras are placed outdoors at points of entry around the campus, the availability, quality, and direction of the light will be everchanging. This makes it difficult to guarantee the success of the facial recognition results, thus preventing the video solution from working correctly.

One of the most common challenges for surveillance cameras is capturing usable footage in low or no light scenarios. Without consistent, adequate illumination, even IP and Internet-of-Things (IoT) cameras cannot effectively record clear enough images for facial recognition to identify entrants or possible threats.

While some cameras come conveniently equipped with builtin LEDs that encircle the lens, they often come with drawbacks. The range for visible LEDs built into a camera is around 150 feet, typically covering a 30-degree field of view (FOV) even though a standard camera FOV is often 90-degrees. This creates “hot spots” in the middle of the camera’s view and can cause a total “white-out” of the rest of the image.

BEST PRACTICES AND KEY TAKEAWAYS

There is no “one-size-fits-all” lighting option for facial recognition deployments, there are a few things end users and system integrators should keep in mind. Consider these key takeaways when deploying lighting for your visitor management system:

Angle of illumination. Consider is the angle of illumination when deploying an external lighting solution. Every camera has a unique FOV, making it important to choose a lighting option that best matches the camera’s requirements.

External versus built-in. While convenient, cameras with builtin LEDs are prone to hot spots, attract bugs, and are susceptible to heat buildup. External illuminators, however, can minimize heat accumulation and allow integrators to adjust the angle of illumination and pair any given camera lens with the perfect range/wavelength for the application.

White light versus IR. For deployments where full-color video is critical day or night, like facial recognition or other analyticsheavy applications, white light is the optimal choice.

This article originally appeared in the October 2020 issue of Security Today.