I travel constantly and internationally. Whenever I return to the United States, I enjoy using Global Entry, which is like E-ZPass for customs. Here’s what it’s like: As you approach customs, you see a sign that says “Global Entry,” which leads you into a corral formed by retractable belt stanchions containing connected kiosks. A uniformed TSA agent tells you to stand in front of one of the stations, which takes your picture in about 2 seconds. The kiosk then tells you to proceed to the customs agent, who just waves you through. While entering a major airport like LAX or SFO from abroad normally involves 30 minutes in a massive line, Global Entry gets me through in 3 minutes. Technically, it’s not that big a boon. It merely means that I get to baggage claim way before most others on my flight, where I wait for 45 minutes for the bags. In any event, face recognition is the secret sauce of fast customs entry. Facial recognition works. It’s super handy for unlocking smartphones and computers, enhancing security through access control and surveillance, streamlining airport and border checks, enabling secure financial transactions, improving patient identification in healthcare, personalizing retail and customer experiences, supporting law enforcement in suspect identification, and facilitating fast check-ins at events and entertainment venues. But many oppose facial recognition technology because it jeopardizes privacy, civil liberties, and personal security. It enables constant surveillance and raises the specter of a dystopian future in which people feel afraid to exercise free speech. Another issue is that one’s face can’t be changed like a password can, so if face-recognition data is stolen or sold on the Dark Web, there’s little anyone can do about the resulting identity theft and other harms. But as people fret and debate about the tradeoff between convenience and security on the one hand, and privacy risk and dystopia on the other, the surveillance tech industry has quietly moved beyond face recognition. Facing the reality of the new biometrics A company called Veritone is succeeding in the market for its Track product. As with face recognition tech, Veritone Track uses AI to spot, identify, and follow people. But unlike facial recognition, it uses body size, gender, hair color, clothing, accessories, and more. Police departments that use Track can choose attributes from a menu (specific types and colors of clothing, hats, backpacks, and other items), and then the system shows them video clips of people matching the user-selected criteria. The system can also track vehicles by make and model, rather than by license plate, and it can stitch together clips from different cameras (CCTV, body cameras, drone footage, social media, and smartphone uploads) to build a timeline of location. More than 400 clients, including police departments, universities, the Department of Justice, the Department of Homeland Security, the Department of Defense, and others, are already using Track, Veritone says. Track is part of Veritone’s iDEMS (Intelligent Digital Evidence Management System), which itself is powered by the aiWARE platform, which brings together more than 300 different AI models (like those for transcription, object detection, etc.) to extract insights from otherwise unrelated data sources, according to the company. Veritone’s offering isn’t alone. Clothing and attribute recognition technology is available through various APIs and platforms, often provided by companies specializing in computer vision and AI. Companies like Ximilar, Clarifai, Google, Amazon, and Microsoft offer identification based on attributes other than face recognition. In addition to specific-person identification, these companies use such technology for product tagging, personalized shopping recommendations, fashion trend analysis, and enhanced image search. Other services combine face recognition with the recognition of people based on clothing and other attributes. In fact, you probably use these technologies all the time whenever you use Google Photos or Meta’s Facebook. Google Photos, for example, analyzes photos taken together in time and detects if a person is wearing the same clothing across multiple photos, even if their face is not visible. The system also considers visual cues and other attributes to help identify individuals across multiple photos taken within a similar timeframe. I once uploaded a picture of two nieces, which I had taken with my iPhone, to Google Photos. When I search for myself, this picture shows up. Why? One of my nieces is wearing mirror sunglasses, and a reflection of my arm is visible in them. Meta developed and uses algorithms that can identify people in photos even when their faces aren’t visible by analyzing visual cues like clothing, hairstyles, and body language. Both platforms use a combination of facial recognition, clothing, and other attribute recognition, as well as contextual information, to improve the accuracy of identifying individual people. What’s also worth noting is that they do this entirely without the subjects’ permission. For example, I could take a picture of someone who doesn’t even have a Google account, then label their face in Google Photos with their name. Many active Google Photos users label dozens of people in their Photos lineup, and most of these people do have Google accounts, which Google could match to their photos. The new world of identification Worry about face recognition tends to obscure the real situation we all find ourselves in: Face recognition is now just one of many ways new technology can identify you. You can be identified by your gait (how you walk). And surveillance cameras now use AI-powered video analytics to track behavior, not just faces. They can follow you based on your clothing, the bag you carry, and your movement patterns, stitching together your path across a city or a stadium without ever needing a clear shot of your face. The truth is that face recognition is just the most visible part of a much larger system of surveillance. When public concern about face recognition causes bans or restrictions, governments, companies, and other organizations simply circumvent that concern by deploying other technologies from a large and growing menu of options. Whether we’re IT professionals, law enforcement technologists, security specialists, or privacy advocates, it’s important to incorporate the new identification technologies into our thinking, and face the new reality that face recognition is just one technology among many.