Flock Safety, the maker of automatic license plate reader cameras used by police across the US, has publicly stated it will not add facial recognition to its devices. Flock CEO Garrett Langley told a crowd recently: “We will not add facial recognition to our devices.” Yet, a third-party firm called VIDIZMO is telling law enforcement it can take the footage Flock cameras collect and run facial recognition on it.
VIDIZMO markets a service that exports video from FlockOS to its own platforms. These tools claim to perform facial recognition, predict behaviour, and analyse race and gender on live feeds. The company has offered facial recognition technology to police departments within the last six months. In May, a salesperson emailed Michael Adams, the deputy police chief in Johnson City, Tennessee. The message claimed VIDIZMO could apply facial recognition to both Flock and Axon data inside a single searchable interface.
“Flock Safety generates plate reads and clips continuously,” the salesperson wrote. “Your investigators use that data on active cases. But that Flock data does not connect automatically to your Axon evidence system.” The email argued that VIDIZMO’s Intelligence Hub would bridge this gap. It would combine Flock Safety data, Axon body-worn camera footage, and other evidence sources into one place. Investigators could then search all data simultaneously by face, vehicle, or object in seconds. DeFlock Johnson City obtained the email via a public records request and shared it with 404 Media. The Johnson City Police Department stated it had not taken a call with VIDIZMO.
“VIDIZMO Intelligence Hub closes that gap. It brings Flock Safety data, Axon body worn camera footage, and any other evidence source into one searchable platform. Investigators search across all of it simultaneously — by face, vehicle, or object in seconds.”
Nadeem Khan, CEO of VIDIZMO, told 404 Media in a video call that the company has not yet run facial recognition on footage from Flock cameras. He admitted the company has not built the specific tool to export data from Flock’s system to its own platform. The company advertises this capability online and already sells other facial recognition products. Khan said VIDIZMO “would love to do the integration.” He believes facial recognition “is the way the world is going, the way the world will have to be.” He suggested Flock should offer the capability itself in a way that “balances privacy, security, and the freedom that we enjoy in this country.”
“It is completely in line with what we want to do,” Khan said. “All of these technologies are already implemented [in the real world]. I think Flock is trying to get out of the way rather than trying to implement the technology right [correctly] by saying they will not do recognition. But this is the way the world is going, it is the way the world will have to be, but with the right sort of technologies where privacy and security are balanced.”
VIDIZMO’s online documentation claims it can ingest data from Flock’s automatic license plate reader (ALPR) and livestream cameras into several of its own products. These products run further AI analysis on the data. Khan wrote in an email: “An agency can import plate reads or clips that it has exported under its own authorized access.” He added that Flock has not been involved in or informed of this. The company’s documentation contains extensive information about its facial recognition and AI analysis products. It includes a page about integrating data from FlockOS — Flock’s “real time crime center” product — into AI Live Insight, Nexus, and AI Intelligence Hub.
“Detection tells you a person is in frame,” VIDIZMO says. “Face recognition tells you which person. It is the capability that takes a face seen on a live camera and matches it against the people you enrolled in the Object Library, so the moment someone on your watchlist walks past a camera, the system names them, on the video, in the event feed, and in the recording, without an operator having to recognize the face themselves.”
The company’s “AI Live Insight” page also claims it can detect “situations” such as trespassing, or “behaviour” by individual people. It turns raw video into behavioural and situational understanding. “Instant alerts fire from the live pipeline with snapshots attached,” the website states. “A second layer runs your queries over recorded events: a plate and a person together.” It notes that enrolled people and objects are recognised on live feeds, with named alerts and snapshots for review.
VIDIZMO already works with several police departments and government agencies. Its documentation states that police departments must upload individual faces as “objects” in the software’s library. These can be added to a “watchlist” that automatically triggers tracking and recording when they are detected by a camera. “With faces enrolled and recognition on, the camera starts naming people as they appear,” the documentation says. “The recognized person’s name appears on the bounding box around their face.” If a user turns on Create Recording, a clip is captured around each recognition. The company says police can set a “Match Threshold,” a numerical confidence score regarding the person’s identity.
The company also tells cops it can automatically try to classify faces by “age, gender, and race.” Police can search by these categories, which are notoriously inaccurate in facial recognition systems.
“By running face detection, you can search for and identify specific individuals in your media or evidence via attribute filters […] You can identify individuals from seven races: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino Hispanic,” VIDIZMO says on its website. The company adds, “Law enforcement agencies can save time analyzing security, CCTV, or dashcam footage. If they have a description of the suspect, such as their supposed age, gender, or race, they can utilize attribute filters to yield effective results.”
VIDIZMO says that in other contexts, cities or stores can use its products to quietly do “demographic analysis” for marketing purposes. This involves determining which races or ages of people are showing up to specific events or to specific stores. “By analyzing the activity in their store, sellers can determine which group spends the most time and makes the most purchases of their products,” the company states. “Getting insights such as these can aid them in tuning their marketing strategy.”
Chris Gilliard, a privacy expert and author of the upcoming book Luxury Surveillance, called the facial recognition product “appalling.” He told 404 Media: “I’m appalled at the willingness of VIDIZMO to tout their capabilities to filter along the lines of race, age, and gender. There’s decades of scholarship that show why this is not possible, and even more so not desirable.” He noted that race and gender are not static categories to be determined by a computer.
The fact that a third-party company is advertising that it wants to do facial recognition on Flock data highlights a common trend in the surveillance space. When one company draws a line in the sand, saying they are unwilling to do a certain type of surveillance, other lesser-known startups try to differentiate themselves by offering that functionality. Because of this recurring phenomenon, it is exceedingly difficult to build and scale a surveillance apparatus and then have companies themselves, in this case Flock, dictate what it can be used for. VIDIZMO’s Khan said, “As a small company who is running without a VC, we have to provide alternatives to the industry. What we are doing is providing that alternative.”
Khan claimed his technology could also be used to preserve privacy. He said facial recognition can be used to redact bystanders’ faces from footage automatically. He stated cops cannot use the system without a specific case number, that all of their actions in the system are recorded for potential audits, and that VIDIZMO collects no data itself. “Much of the harm your reporting has documented comes from implementation choices, not from the technology itself,” Khan said. “Those choices include pooling data into a nationwide network, allowing searches without a case, keeping audit logs no one can meaningfully inspect, and treating an AI match as an answer.”
Gilliard said that the fact that facial recognition and other AI analysis can be added to existing surveillance cameras highlights the importance of not building such systems in the first place.
“What Flock says about their capabilities is to some degree irrelevant because they have built the infrastructure for mass surveillance. Their entire existence provides the foundation for it.”
What it means
Police departments can now bypass a vendor’s refusal to build surveillance tools by buying third-party software that connects to the same cameras. This makes it difficult for hardware makers to control how their data is used once it leaves their systems.
