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As Flock continues to be the center of a nationwide conversation about automatic license plate reading cameras, the company has said that it doesn’t, and won’t, do facial recognition: “We will not add facial recognition to our devices,” Flock CEO Garrett Langley said in a recent video. But other, third-party companies are telling cops that they are willing to add facial recognition to data gathered by Flock cameras in order to “close that gap.”
A company called VIDIZMO is advertising the capability to export footage from FlockOS to its own platforms that do facial recognition, behavior prediction, and racial and gender analysis on live camera feeds. VIDIZMO is a several decades-old video analysis and database company that has started offering facial recognition technology to cops within the last six months. In a May email to Johnson City, Tennessee, deputy police chief Michael Adams, a salesperson from VIDIZMO wrote that the company’s product could do facial recognition on both Flock and Axon data in “one searchable platform.”
“Flock Safety generates plate reads and clips continuously. Your investigators use that data on active cases. But that Flock data does not connect automatically to your Axon evidence system,” the salesperson wrote. “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.” The email was obtained using a public records request by DeFlock Johnson City and was shared with 404 Media. The Johnson City PD told 404 Media in a statement that it did not take a call with the company.
VIDIZMO’s CEO, Nadeem Khan, told 404 Media on a video call that the company has not yet started running facial recognition tech on footage from Flock cameras, and that it has not actually built the specific tool that would export data from Flock’s system to its own platform (though the company advertises this capability online, and the company already sells other facial recognition products). But Khan said VIDIZMO “would love to do the integration,” and that he believes facial recognition “is the way the world is going, the way the world will have to be,” and that he thinks 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.”
In marketing material and technical documentation available online, VIDIZMO does claim that it can ingest data from Flock’s automatic license plate reader (ALPR) and livestream cameras into several of its own products, which do further AI analysis on the data. “An agency can import plate reads or clips that it has exported under its own authorized access,” Khan said in an email. “Flock has not been involved in or informed of this.” VIDIZMO’s online documentation for customers has extensive information about its facial recognition and AI analysis products, and contains a page about integrating data from FlockOS — Flock’s “real time crime center” product — into AI Live Insight, Nexus, and AI Intelligence Hub, which are three different VIDIZMO products.
“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 that it can detect “situations” such as trespassing, or “behavior” by individual people, “turning raw video into behavioral and situational understanding.”
“Instant alerts fire from the live pipeline with snapshots attached. A second layer runs your queries over recorded events: a plate and a person together,” VIDIZMO’s website says. “Enrolled people and objects recognized on live feeds, with named alerts and snapshots for review.”
VIDIZMO already works with several police departments and government agencies, according to its website. The company’s documentation says that police departments need to upload individual faces as “objects” in the software’s library, which 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 you turned on Create Recording, a clip is captured around each recognition.” The company says that police can set a “Match Threshold,” which is a numerical confidence score about the person’s identity.
The company also tells cops that it can automatically try to classify faces by “age, gender, and race,” and that cops 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 also use its products to quietly do “demographic analysis,” for marketing purposes, by 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. Getting insights such as these can aid them in tuning their marketing strategy.”
The facial recognition product it offers is “appalling,” according to Chris Gilliard, a privacy expert and author of the upcoming book Luxury Surveillance.
“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 told 404 Media. “Particularly in the case of race and gender, which 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 that his technology could also be used to preserve privacy; facial recognition, he said, can be used to redact bystanders faces from footage automatically. He said that 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 other perhaps even more invasive technologies to be built on top of it,” Gilliard said. “Flock itself needs to be understood as part of the surveillance ecosystem that exists in this country. This is why discussions of guardrails for a particular system are inadequate because another company can (and will) come along to assemble another layer of surveillance on what has already been established.”
Flock and Axon did not respond to a request for comment.
