We're all too familiar with the notch—the unsightly cut-in that graced many smartphones for years, like the iPhone X or the LG G7. The notch has largely been replaced on today’s smartphones by floating punch-hole cameras that take up less space and look a little more futuristic, though notches are still prevalent on some laptops, like Apple’s MacBooks. On the iPhone, Apple calls its floating pill-shaped camera system the Dynamic Island, which debuted on the iPhone 14. The iPhone still has the largest camera cutout today, due to its Face ID biometric authentication system. (Barring Google Pixel phones, the vast majority of Android phones don't offer a secure face authentication equivalent, so they don't need a bulky camera cutout.) This island could get much smaller, however, thanks to new under-display camera technology announced at Display Week 2026 from Metalenz, a optics startup from Boston. A Primer on Metasurfaces Metalenz’s optical metasurfaces technology is a flat-lens system that uses a fraction of the space of traditional multi-lens elements in most smartphones. You can read more about it in our original coverage of the company here, but in short, instead of refracting light through multiple plastic or glass lens elements—which improves image clarity, corrects aberrations, and brings more light to the camera sensor—metasurfaces use a single lens with nanostructures to bend light rays toward the sensors. Metalenz says more than 300 million of its metasurfaces are already used in consumer devices today, replacing bulky traditional optics in time-of-flight sensors that capture depth information and assist with a camera's autofocus. The company also pioneered a method to use these metasurfaces to capture polarization data. When light hits an object with specific material properties, it creates a unique polarization signature. Light reflecting off black ice has a different polarization signature from light reflecting off
May 4, 2026 · via wired.com
When Henry VIII married Anne of Cleves, historical records state he was so appalled that her beauty did not match the portrait by Hans Holbein the Younger that his fourth wife became widely known as the “Flanders mare”. Now, Holbein’s pictures of another of Henry’s wives, Anne Boleyn, have been called into question using a more scientific method of analysis: artificial intelligence (AI). Researchers used facial recognition tools to establish that a sketch of a previously unknown woman could be of Henry VIII’s doomed second wife. Meanwhile, a drawing previously thought to be of Boleyn by Holbein in the Royal Collection has probably been misidentified and is more likely to be a picture of her mother. Academics used AI to re-examine the second drawing, known as the Windsor Sketch, which for 200 years has been assumed to be the Tudor queen based on an 18th-century inscription that says it is “Anna Bollein Queen”. The portrait, labelled as Anne Boleyn (c.1500-1536), depicts a blonde woman with a double chin. This contradicts contemporaneous accounts of Boleyn, which describe her as slender, dark-haired and with a distinctive “little neck”, a feature she reportedly referenced before her execution. Using facial recognition software, experts went on to suggest that another drawing by Holbein the Younger in the Royal Collection is more likely to be Boleyn. Karen L Davies, a historian who led the study, which is published in Nature Heritage Science, said: “I’ve spent many years researching Anne Boleyn, and what ultimately drove me to this project was a growing dissatisfaction with the long-accepted identification. There were simply too many contradictions. “The drawing didn’t align with the primary sources and each proposed explanation seemed only to generate further inconsistencies rather than resolve them.” Davies and academics from Bradford and Stanford Universities set out to use
May 4, 2026 · via thetimes.com
A new report reveals that children across the UK are outwitting online safety measures with fake birthdays, borrowed IDs, and some surprisingly creative facial hair. A third of children say they have bypassed online age checks in the past two months - some by drawing fake moustaches on their faces to trick facial recognition software. The report from Internet Matters titled The Online Safety Act: Are Children Safer Online? surveyed 1,270 children aged 9-16 and their parents across the United Kingdom to see whether the country's landmark online safety legislation is delivering any meaningful protection for children. One mother told researchers she caught her son using an eyebrow pencil to draw a moustache on his face to pass a platform's facial age estimation check. It worked. He was verified as 15. He was 12. What did the report find out? The study discovered that 46% of children believe age checks are easy to bypass, while only 17% say they are difficult. Among the infiltration methods children described were entering a fake birthdate, using someone else's identification, submitting videos of other people's faces, and using video game characters to fool facial recognition tools. "I've seen clips of people online where they'll get clips of video game characters like turning their head and use it for age verification," one 11-year-old girl told researchers. Older children were more confident about circumventing checks, with 52% of those aged 13 and over saying age verification is easy to beat, compared with 41%of those aged 12 and under. The most common reasons children gave for bypassing age checks were to access a social media platform they were not old enough to use (34%), to join an online game or gaming community (30%), and to use a messaging app (29%). The report also found that just over
May 4, 2026 · via euronews.com
Trivia Quiz·Disney Quiz·Posted 15 hours agoYou Think You Know Disney? I Bet You Can’t Even Name Half Of These MoviesGuess the movie: ❄️👸🏰by Lauren GarafanoBuzzFeed StaffcommentFacebookPinterestLink Here's the deal: I turned all your favorite Disney movies into emojis. Some are suuuuuuuper easy. And some...well...good luck! Do you think your pattern recognition skills can help you correctly identify these Disney movies? Let's find out! Share This QuizFacebookPinterestLinkComments
Comments
May 4, 2026 · via buzzfeed.com
Abstract We propose ADGNET, a semi-supervised framework for Alzheimer’s disease (AD) diagnosis that jointly optimizes image reconstruction and classification through shared feature representations. The architecture integrates a residual backbone with attention modulation for dynamic feature selection, an encoder-decoder reconstruction branch for unsupervised representation learning, and a classification branch with focal loss to address class imbalance. This dual-task design enables effective feature learning from limited annotations. On two public MRI datasets—KACD (2D, 6,400 images) and ROAD (3D, 532 scans)—ADGNET achieves average performance improvements of 4.1% and 7.2% over state-of-the-art methods (ResNeXt WSL, SimCLR) across six metrics. Interpretability analysis using Grad-CAM and attention visualization confirms that the model focuses on clinically relevant neuroanatomical structures, particularly the hippocampus and temporal lobes, with strong correlation to established AD pathology (r = 0.67, p < 0.001). These results validate the model’s exceptional generalization capability and feature representation effectiveness across multi-modal medical imaging data, offering an efficient solution for few-shot medical image analysis. Citation: Yang X (2026) Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy. PLoS One 21(5): e0348596. https://doi.org/10.1371/journal.pone.0348596 Editor: Vince Grolmusz, Eotvos Lorand University: Eotvos Lorand Tudomanyegyetem, HUNGARY Received: September 6, 2025; Accepted: April 17, 2026; Published: May 4, 2026 Copyright: © 2026 Xiaobo Yang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript and its Supporting Information files. Funding: The study is supported by Zhejiang Province Natural Science Foundation, grant number Y1110023 to XY. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests
May 4, 2026 · via journals.plos.org
If you’ve tried using Claude to learn to code and walked away feeling like you just witnessed a magic trick rather than actually learned something, you’re not alone. Without structure, AI coding tools default to doing the work for you—you end up with hundreds of lines of code without understanding the algorithm or the logic behind it. That said, after much trial and error, I’ve finally perfected a prompt that turns Claude into an exercise-driven tutor. It focuses on teaching concepts by making you work through the code, while also remembering your progress and difficulties to refine future sessions. Best of all, it only takes two minutes to set up. How you can use Claude vibe coding to learn actual coding The daily workflow and context tracking It’s not exactly news that you can use an LLM as a tutor. Being able to ask questions in natural language and get a relevant, contextual answer is a genuine game changer for self-learners. The experience is fundamentally different from—and often better than—Googling something. With search, you had to frame everything as keywords and hope someone had already written about your specific problem in a way that made sense. With an LLM, you just ask, and it explains the concept in a way you can actually relate to. Artificial intelligence basics Trivia challenge From chatbots to neural networks — find out how much you really know about AI. What does the term 'machine learning' most accurately describe? Who is widely credited with coining the term 'artificial intelligence' in 1956? What type of AI model powers popular chatbots like ChatGPT? What is 'overfitting' in machine learning? What is 'AI bias' most commonly referring to? What does 'GPT' stand for in AI model names like GPT-4? Which of the following best describes 'deep learning'? What
May 4, 2026 · via howtogeek.com
by Matthew R. Jewell When many hear the term “AI,” their thoughts go to some massive computer system that has all the answers. In reality, AI is an umbrella term that includes a variety of computer/software systems. Today, the most notable are Large Language Models (LLMs), such as ChatGPT. Not as well known, but in high use, are Robotic Process Automations (RPAs). While there are other AI tools performing facial recognition or producing songs, LLMs and RPAs have the potential for the most applicability to contracting and, therefore, adding to the contracting toolbox. An LLM can assist contracting professionals in producing the many documents that make up a contract file. Through a series of questions and responses, or prompts, a properly trained or configured LLM can assist the contracting professional in writing just about any document. With regard to RPAs, we already have an example in the contracting toolbox with the Determination of Responsibility Assistant (DORA) bot. Simply put, RPAs automate an existing process. Much like other tools before them, AI tools reduce the toil and time associated with completing a task. In this regard, they are no different than Microsoft Excel or computer aided design (CAD). Not specific to a particular industry or profession, both assist those with knowledge, skill and ability in a particular field to complete their work more efficiently, with increased reliability and quality. These tools do not replace the user’s core competencies, rather, they enhance their abilities. AI tools can generate something “new” or identify patterns that are not readily discernable to a human. Imminently useful across domains, these aspects are especially helpful to the contracting professional. With a properly trained or configured LLM and good prompt engineering, a contracting professional can produce a new product in a fraction of the current time required. Note
May 4, 2026 · via dvidshub.net
The Troy City Council has drafted a local law establishing standards for the use of automated license-plate reading cameras following its recent conflict with Mayor Carmella Mantello over the city's contract with Flock cameras. Crafted by Councilperson Noreen McKee, the local law will be introduced for discussion at the council's Thursday meeting. A public hearing on the legislation is tentatively set for 5:30 p.m., May 21 at 5:30 in Troy City Hall. Mantello, the Rensselaer County district attorney and Troy police chief came out against the measure on Monday. The council says the camera systems collect detailed data on people without their knowledge and poses risks to privacy, civil liberties and the freedom of movement if unregulated. To "protect residents from government surveillance and to maintain public trust in city operations," the proposed law would limit the city's use of automated license-plate reading (ALPR) cameras and the transfering of plate data to certain conditions, such as in connection with an investigation, a crime or a missing person. It would require plate data obtained by the city to be permanently deleted within 48 hours, except in certain legal situations. It would also require any city department or agency that operates or uses the system to post an annual report on the city's website and maintain a log of every query for three years. “The council supports the important work of the Troy Police Department to investigate crime in our community, but as this technology continues to rapidly evolve, we must ensure protections and oversight are in place,” council president Sue Steele said. The legislation also sets a penalty for persons injured by violations of the proposed law by entitling them to damages for mental pain and suffering, or $1,000, whichever is greater, reasonable attorneys' fees and costs of litigation. The law
May 4, 2026 · via spectrumlocalnews.com
The Syracuse Common Council will vote on its biometric law in two weeks, after it was tabled at their meeting on Monday. That law would prevent businesses from using biometric identification surveillance systems. According to the American Civil Liberties Union (ACLU), those systems can collect and analyze various information, including the shape of people's faces and eyes, along with their voice and even how they walk. "Biometric scanning makes a lot of mistakes based on people that look a little bit alike, but imagine were it makes mistakes where people don't look anything alike, and they're just tracking skin color or how far apart your eyes, the size of your lips, the size of your nose, it's scary to me," Jimmy Monto, 5th District councilor, said. Banks would be exempt from the law given safety concerns. The addendum to existing laws was sponsored by Councilors Monto, Chol Majok and Corey J. Williams. Erie County passed a similar law last week. The proposal comes after Wegmans recently deployed cameras equipped with facial recognition technology at select stores, saying some stores are using the technology to help with misconduct and retail theft, and that it doesn't collect other biometric data and disposes of the images and video after security purposes are fulfilled.
May 4, 2026 · via spectrumlocalnews.com
It happened in a flash outside Barclays in Croydon town centre. A digital trap snapped shut around one of Britain’s thousands of wanted criminals. In little over a minute, a combination of high-definition cameras, automated AI face scanning and half a dozen police officers had run a wanted man to ground. After the handcuffs clicked shut, the Metropolitan police’s controversial live facial recognition (LFR) cameras had chalked up another arrest: the fifth in 45 minutes on a regular Thursday morning. The arrest was one of hundreds made during a six-month Met police pilot of LFR cameras on vans and fixed to lamp-posts, as also seen across cities in China, the UAE, India and Israel. Critics have called the technology invasive, unregulated and anti-democratic, cited studies suggesting racial bias and called for it to be scrapped. But the Met police commissioner, Mark Rowley, has said it is “gamechanging” and keeps the public safe. The trap was set at 10am, when the clusters of surveillance cameras mounted high on pillars at the junction of Church Street and North End were switched on. Standing nearby was Kevin Brown, a plain-clothes police sergeant. The wanted man unwittingly walked past one of the cameras and instantaneously Sgt Brown’s handheld beeped furiously and flashed up the suspect’s pin-sharp live photo, alongside a previous custody photo, his name, the suspected crime and warnings of any weapons or drugs risk. Every face passing the cameras – as many as 5,000 an hour – was being scanned and its biometric data streamed live to a police operations room five miles away in Sydenham. There, an AI-powered system, supplied by the Japanese tech company NEC, checked it instantly against photos of wanted suspects and people under court orders. Brown had a match. Uniformed officers standing across the road received the
May 4, 2026 · via theguardian.com
Hof – Today, artificial intelligence can describe images, recognize objects, and explain complex relationships. The pace of development is remarkable: so-called vision-language models (VLMs) combine text and image understanding in impressive ways. Yet, of all things, they struggle with a seemingly simple task—counting. Researchers at the Institute for Information Systems (iisys) at Hof University of Applied Sciences are now working to address this issue. “Many common models are very good at recognizing what can be seen in an image—but not reliably how many objects there are,” explains Prof. Dr. René Peinl from the Institute for Information Systems (iisys) at Hof University of Applied Sciences. Errors become more frequent when there are more than four or five objects of the same type. Why Counting Is So Difficult for AI The problem runs deeper than it may appear at first glance. While humans can intuitively grasp small quantities, larger numbers must be actively counted. This crucial step is missing in many AI models. In addition, existing training data is often unsuitable. “Some datasets are too simple and only encourage pattern recognition—others are too complex or flawed, for example due to occluded objects or ambiguous questions,” says institute director Prof. Peinl. As a result, models tend to “guess” or rely on learned expectations—sometimes producing surprisingly incorrect results. The Solution from Hof: An Artificial Dataset To tackle this problem in a targeted way, iisys has developed the SITUATE dataset. Instead of using real photographs, the researchers generate artificial 3D scenes with clearly defined properties. “We wanted to create an environment in which we can precisely control what happens in the image—and what does not,” says Prof. Dr. René Peinl. These scenes contain geometric objects such as cubes, spheres, or cylinders, with clearly defined positions (e.g., “to the left of the table”), allowing for targeted
May 4, 2026 · via idw-online.de
TheLostSwede News Editor - Joined - Nov 11, 2004 - Messages - 21,014 (2.68/day) - Location - Sweden | System Name | Overlord Mk MLI | |---|---| | Processor | AMD Ryzen 7 7800X3D | | Motherboard | Gigabyte X670E Aorus Master | | Cooling | Noctua NH-D15 SE with offsets | | Memory | 32GB Team T-Create Expert DDR5 6000 MHz @ CL30-34-34-68 | | Video Card(s) | Gainward GeForce RTX 4080 Phantom GS | | Storage | 1TB Solidigm P44 Pro, 2 TB Corsair MP600 Pro, 2TB Kingston KC3000 | | Display(s) | Acer XV272K LVbmiipruzx 4K@160Hz | | Case | Fractal Design Torrent Compact | | Audio Device(s) | SteelSeries Arctis Nova 3 Wireless | | Power Supply | be quiet! Pure Power 12 M 850 W | | Mouse | Logitech G502 Lightspeed | | Keyboard | Corsair K70 Max | | Software | Windows 11 Pro | | Benchmark Scores | https://valid.x86.fr/yfsd9w | Asustor Inc. today is announcing that it will unveil a range of new products at Computex 2026 in Taipei, to showcase its superior network storage prowress with numerous models in addition to its award winning lineup of network storage solutions. Flashstor Gen3 Series - Flagship Flash NAS for Creators with Optional AI The all-flash NAS, highly praised by professional photographers and video creators, is receiving a significant upgrade. The new Flashstor Gen3 series is equipped with an AMD Ryzen 5 Pro 8640U Six-Core processor, providing 16 TOPs of AI computing power. Enterprise-Grade Petabyte-Level Storage Environment - The Lockerstor R Pro Gen2 and the Xpanstor 12R Gen2 Join Forces To meet the stringent requirements of enterprises for high data storage and high availability, the newly launched Lockerstor 24R Pro Gen2 which is 4U and 24-bay in addition to the rest of the Lockerstor
May 4, 2026 · via techpowerup.com
Foremost, a passive data trail is information collected automatically by devices, networks, or systems as a byproduct of normal operation—like signals, logs, or metadata. Devices that leave passive data trails 1. Smartphones- These are the biggest contributors. As they offer- 1.) Cellular signals: Your phone constantly communicates with nearby cell towers (even when idle). 2.) Wi-Fi scanning: It probes for known networks, revealing device identifiers. 3.) Bluetooth: Emits signals that can be picked up by nearby devices (used in tracking beacons). 4.) Sensors: GPS, accelerometer, gyroscope generate location and movement data. Even with no apps open, your phone is still “talking” to networks. 2. Wearables (Smartwatches, Fitness Bands) Devices like smartwatches passively collect: > Heart rate and health data > Movement and sleep patterns > Location (if paired with phone or GPS-enabled) They sync periodically, creating background data logs. 3. Laptops & Computers a.) Connect to Wi-Fi networks and log IP addresses b.) Background services send diagnostic or usage data c.) Browsers track activity via cookies and scripts Even idle computers can generate network traffic. 4. Smart Home Devices (IoT) Examples: Smart speakers, Smart Televisions, Security or CCTV cameras, Smart thermostats They passively generate: 1.) Usage patterns (when you’re home, what you watch) 2.) Voice snippets (in some cases) 3.) Device interaction logs 5. Vehicles (Modern Connected Cars) Modern cars collect: A.) GPS location B.) Driving behavior (speed, braking) C.) System diagnostics Some connect to manufacturer servers or apps automatically. 6. Public Infrastructure Interactions Even without owning a device, passive trails happen via: i) CCTV cameras (facial recognition in some places) ii) Automatic toll systems (RFID tags) iii) Public Wi-Fi networks (device tracking via MAC addresses) 7. Payment Systems A.) Contactless cards and mobile wallets B.) Transaction logs (time, place, amount) Even a tap-to-pay leaves a digital trace. 8. Bluetooth
May 4, 2026 · via cybersecurity-insiders.com
About
Press
Copyright
Contact us
Creators
Advertise
Developers
Terms
Privacy
Policy & Safety
How YouTube works
Test new features
NFL Sunday Ticket
© 2026 Google LLC
May 4, 2026 · via youtube.com
AI and the Everyday: An Introduction to Focussing on Everyday Interactions of AI How can we highlight new ways in which researchers can further their engagement with AI in the Global South? During a workshop titled Public Debates, Everyday Injustice, and AI in the Majority World, a group of researchers and activists discussed how researchers should take power asymmetries, hierarchies, and inequalities into account, both across and within the global South. This introductory article kicks off the online continuation of their discussion. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Focussing on ‘the everyday’ gives us valuable insights into how AI shapes routine interactions in sites of work, leisure, education, and homes. It shows us the pervasiveness of AI-technologies and the various ways in which people negotiate it. Insights into the everyday provide an understanding of the messiness and the ambiguities that shape people’s responses to AI as they encounter its development, deployment, and use. In many of these interactions, engagements with AI are nested within discussions of opportunity, aspiration, and hope. These discussions are situated within a context where states and corporations propel a narrative of the inevitability of AI. However, people engage with AI from different standpoints. These standpoints include people's engagement with AI as actors with the agency to subvert, resist, and creatively challenge or use the power of AI, but also as those who are used, surveilled, and dominated through the ubiquitous use of AI-systems. What kinds of relations emerge with the use of AI? How can one capture and understand these relations? In this piece, we introduce and reflect on entries from an article symposium that arose from a workshop titled Public Debates, Everyday Injustice, and AI in the Majority World. These entries explore how people negotiate with AI in a
May 4, 2026 · via diggitmagazine.com
4 May 2026
UK report sparks dystopian fears with facial recognition
United Kingdom correspondent Diane To spoke to Melissa Chan-Green about how the UK government has said it might ban pro-Palestinian demonstrations as a move to tackle antisemitism and how a new report is sparking dystopian fears with facial recognition. She also spoke about King Charles' royal visit to Bermuda.
May 4, 2026 · via rnz.co.nz
You do not have permission to retrieve the URL or link you requested,
If you think this was a mistake please call 1300 134 174 or e-mail customercare@realestate.com.au
and quote the reference number #18.72cfdb17.1777882455.28ea31b3
May 4, 2026 · via realestate.com.au
In April, the Home Office announced £115m of new funding to create the “Police.AI” centre. Its aim is to introduce a “rapid and responsible” rollout of AI tools across all 43 forces in England and Wales. The National Police Chief Council (NPCC) claims it will establish UK policing as “a global leader in applied, responsible AI”. Currently, each police force in the UK largely determines for itself how and when to procure, trial and deploy AI tools, leading to a lack of standardisation and inefficient, costlier procurement practices. This has inevitably led to concerns being raised regarding policy, ethics and accountability – and nowhere has the debate been more pointed than around the use of Live Facial Recognition (LFR). This is because LFR is already being used at scale. More than 25,000 “retrospective” facial recognition (RFR) searches are carried out per month, with images from CCTV, mobile phones, dashcams, video doorbells and social media compared against a police national database of custody mugshots. In London, between January 2024 and September 2025, over 1,300 suspects were arrested with the help of live facial recognition technology (FRT). In February, the government concluded a consultation on legislation to govern the use of the technology by police. The Home Office signalled its intentions plainly: “The consultation will pave the way for new laws so all police forces can use this new technology with greater confidence and more often.” Up to this point, police advocates have stressed that the patchwork of checks and balances in place, such as the AI covenant in policing, and the Surveillance Camera Code of Practice, are sufficient for regulating the use of LFR. Critics counter that the public are being asked to rely on police assurances in what remains a legal grey area. Questions are also being raised about the
May 4, 2026 · via newstatesman.com
AI-powered image processing enhances daily life in Malaysia, from phone cameras to hospital scans and smart security systems. EVERY time you take a selfie and your phone magically enhances the lighting or when you walk past a security camera that seems to follow your movement, you are witnessing the quiet power of digital image-processing combined with artificial intelligence. These technologies are no longer just part of science fiction; they are part of our everyday lives, often working behind the scenes without us even noticing. Digital image-processing is a way for computers to “see” and “understand” pictures. It helps machines clean up blurry images, recognise faces and even detect objects. When paired with artificial intelligence, which allows computers to learn and make decisions, the results are impressive. Your phone knows where your face is in a photo. A hospital scanner can highlight signs of illness and a traffic camera can identify a speeding car in seconds. All of this is possible because of the way computers process images and learn from them. In Malaysia, these technologies are already being used in many sectors. Hospitals use AI to help doctors read X-rays more accurately. Security systems in shopping malls and offices use smart cameras to detect suspicious behaviour. Even agriculture is benefiting, with drones flying over fields to check crop health using image analysis. It is clear that AI and image processing are helping us live safer, healthier, and more efficient lives. But as these systems become smarter, they also raise important questions. Who controls the data collected by these cameras? How is your face being stored and used? Can these systems make mistakes? These are not just technical issues; they are ethical ones. For example, facial recognition can help catch criminals but it can also be used to track innocent people
May 4, 2026 · via thesun.my
Photos are an expected part of almost any visit to Disneyland. Whether it’s first-time international visitors or longtime returnees, just about everyone is clicking photos at some point. That everyone, however, is extending more and more to Disneyland personnel. My colleague Hannah Fry documented the growing use of facial recognition technology at the Happiest Place on Earth. Privacy experts are sounding the alarm, while some patrons are trying their best to avoid the technology. Let’s dive into her reporting to see what’s going on. Under the mouse’s eye Photographs of guests’ faces taken at the entrances to Disneyland and sister park California Adventure are run through biometric technology to convert the images into unique numerical values. The images can then be compared with pictures taken when a customer first used the ticket or annual pass. “Pretty much every other place is doing the same thing,” said John LeSchofs, 73, who visits the park roughly every six weeks with his wife. “The police, the government, they’re all using facial recognition. I don’t think it’s going to stop.” Disney officials say the technology helps make entering and reentering the park easier and prevents fraud. Sounding the alarm The rapid growth of facial recognition over the last decade has raised concerns among privacy experts who caution that such data can easily be turned over to law enforcement entities or make companies hacking targets. “The normalization of facial surveillance is really problematic,” said Ari Waldman, a professor of law at UC Irvine. “We can’t go around life hiding our faces, so this isn’t just [the] next step in surveillance; it’s qualitatively different. In a world of facial recognition, when people leave their house, it automatically means they’re identified.” Following a familiar pattern Venues over the last decade have increasingly relied on facial recognition to
May 4, 2026 · via union-bulletin.com