In this article series, we’ve taken a look at three data-intensive technologies that are giving law enforcement agencies unprecedented access to mission-critical information in the field, at the station, in the crime lab and in the real-time crime center.
Added to the list — along with body-worn cameras, drones and automated license plate readers — is a lesser-used but powerful technology that has shown the potential to quickly help narrow investigative leads, locate suspects as well as missing persons and verify identities in controlled settings like correctional facilities and border crossings: facial recognition.
From image comparison to investigative intelligence
At its simplest, facial recognition technology compares an image of a face against a database of known images and returns possible candidates. That process can support a range of legitimate public safety needs, from verifying the identity of an unknown person to helping identify a suspect in a serious violent crime.
The most useful way to understand facial recognition is as a tool for narrowing investigative leads. It does not replace an investigator, establish probable cause by itself or eliminate the need for corroborating evidence. Rather, it can help trained personnel quickly sort through a mountain of images and focus their next steps.
Investigators working a violent assault may have a partial image from a security camera. A facial recognition search may return several possible candidates for review. From there, investigators still need to compare the result with other evidence, such as witness statements, location data, records checks, vehicle information or additional video.
In the case of a missing person, a possible match may help investigators identify a person seen on camera at a transit station, hospital or public venue. In unidentified remains cases, facial comparison may be one piece of a broader forensic effort that also includes fingerprints, dental records, DNA and missing persons’ databases.
In a large public event, law enforcement can analyze camera images of thousands of people in the crowd and use facial recognition to help identify potential matches to persons on a watch list — a monumental task made quicker and easier.
Raising the stakes for image verification
However, the task of identifying a potential lead using facial recognition has a new complication — deepfakes. In the past, the central question was whether an image was clear enough to be useful. Increasingly, investigators must also ask whether the image is real, whether it has been manipulated and whether it accurately represents the person or event it appears to show.
As synthetic media becomes more realistic, agencies need workflows that help determine whether an image is authentic and appropriate for use in an investigation and whether the result is supported by other evidence. A possible match should prompt further investigation, not conclude it. Agencies best positioned to use facial recognition effectively during an era of deepfakes will be those that combine technology with clear policy, human review and sound investigative practice.
Why connectivity matters
Facial recognition does not operate in isolation — it’s part of a larger digital ecosystem that includes cloud storage, body-worn camera systems, mobile devices, real-time crime centers, records systems, drone video, fixed cameras, command platforms and AI-assisted analytics. Underpinning all is a robust communications infrastructure that can support immense data loads at speeds necessary to deliver critical information in real time, even in times of network congestion.
An officer in the field may need to upload an image, access a secure application, receive a notification or share information with investigators. A real-time crime center may need to review incoming video from multiple locations while analysts coordinate with patrol. A detective may need to compare images, retrieve case files and collaborate with another jurisdiction. During a large event or critical incident, many users may be trying to access the same network at the same time.
In each scenario, speed and reliability affect the usefulness of the technology. A tool that works well in the station but fails in the field does not fully support the mission. Delayed uploads, buffering video or unreliable access can slow decision-making at the very moment when information is most valuable.
That is where 5G becomes more than a technical upgrade. High-bandwidth, low-latency connectivity supports the real-time movement of data, video and analytics across the public safety environment. It helps agencies take advantage of tools that require rapid access to cloud-based resources, supporting mobility by allowing officers and investigators to work from vehicles, command posts, event venues and incident scenes without being limited to fixed infrastructure.
For facial recognition, 5G can help support faster image transmission, more reliable access to cloud platforms, better integration with mobile devices and more effective coordination between the field and command.
A responsible path forward
Facial recognition will continue to evolve, especially as AI advances and synthetic media become more common. As law enforcement leaders understand, it can be tempting but foolish to adopt the latest technology for its own sake — but a worthy investment if using the right tools for the right cases, with the right safeguards in place.
Facial recognition can be a valuable part of a larger connected future. As we’ve seen in this series, the next generation of law enforcement tools will rely on secure, high-performance networks capable of supporting video, AI, mobile applications and real-time collaboration. As agencies invest in those capabilities, 5G connectivity will play a central role in making advanced technologies practical in daily operations.
T-Priority: Built for data-intensive public safety operations
Facial recognition is just one example of how public safety is becoming increasingly data-intensive. Body-worn camera video, drone operations, real-time crime centers (RTCC), automated license plate readers, and advanced analytics all depend on moving large amounts of information quickly, securely, and reliably.
As agencies adopt these technologies, capacity becomes just as important as coverage. Consider an RTCC during a major incident. Drone video, body-worn camera feeds, fixed security cameras, CAD updates, license plate reader alerts and facial recognition queries may all flow across the network simultaneously. Analysts and responders synthesize these data streams to build situational awareness, identify emerging threats and make informed decisions. Maintaining consistent performance requires more than priority access — it requires sufficient network resources to sustain multiple bandwidth-intensive applications simultaneously.
That’s why T-Mobile developed T-Priority. Built on the nation’s first and largest 5G Standalone network, T-Priority provides first responders with prioritized access using a dedicated network slice that dynamically allocates additional network resources during rare periods of extreme congestion, helping agencies maintain consistent performance when operational demands are greatest.
As public safety becomes increasingly connected and data-intensive, communications infrastructure is no longer simply supporting the mission — it has become a mission-critical component of public safety. Purpose-built for first responders, T-Priority helps provide the dedicated network resources needed to support today’s connected, data-intensive public safety operations.
T-Mobile has been recognized as America’s Best Mobile Network by Ookla®. Capable device required for 5G; coverage not available in some areas. Some uses may require certain plan or feature; see T-Mobile.com. Ookla trademarks used under license and reprinted with permission.
