The Walt Disney Company's decision to introduce facial recognition technology at the entrances to Disneyland Park and Disney California Adventure Park has sparked strong reactions from visitors. The technology aims to speed up entry and reduce ticket abuse. The camera at the entrance scans the visitor's face and compares it to the image recorded during the first use of the ticket. The data is converted into numeric codes for verification and, according to the company, is deleted within 30 days, unless required for legal or security reasons. Mixed reactions Although use of the system is voluntary and alternative entrances without biometric technology exist, many visitors have described the move as "dystopian", comparing it to "Big Brother"-style surveillance. Some have raised privacy concerns, asking why the data is not deleted immediately, while others are particularly concerned about the use of technology by children. Arguments in favor On the other hand, some visitors have welcomed the system, emphasizing that it significantly reduces waiting time in line. They argue that similar technologies are already widely used in various institutions and companies. Security and limitations The company emphasizes that technical and administrative security measures have been put in place to protect data, but acknowledges that no system is completely secure. For children under 18, the use of technology is only permitted with parental approval. A step towards the future or a risk to privacy? The debate remains open between the convenience offered by technology and concerns about the protection of personal data. For many, this is a clear example of the difficult balance between innovation and privacy in the digital age. /GazetaExpress/
May 1, 2026 · via gazetaexpress.com
Explore the things
you love
.
Log into Facebook
Email or mobile number
Password
Log in
Forgot password?
Create new account
English (US)
Español
Français (France)
中文(简体)
العربية
Português (Brasil)
Italiano
More languages…
Sign Up
Log In
Messenger
Facebook Lite
Video
Meta Pay
Meta Store
Meta Quest
Ray-Ban Meta
Meta AI
Instagram
Threads
Privacy Policy
Consumer Health Privacy
Privacy Center
About
Create ad
Create Page
Developers
Careers
Cookies
Ad choices
Terms
Help
Contact Uploading & Non-Users
Meta © 2026
Apr 30, 2026 · via facebook.com
Lawmakers voted Thursday to approve a new local law that prohibits the collection, use and sale of biometric identifier information in commercial settings in Erie County. The "Biometric Transparency and Privacy Act" was approved by a 7-3 vote. In passing the law, Erie County lawmakers said that it is intended to "promote transparency and protect the public." "The Erie County Legislature finds that biometric identifier technologies are increasingly used in commercial settings by commercial establishments for purposes including, but not limited to, customer authentication, building access, and fraud prevention," lawmakers said of the intent of the new act. "The legislature also finds that biometric identifier information in uniquely sensitive because it is derived from a person's physical or biological characteristics and poses serious privacy and security risks to individuals if compromised. The Erie County Legislature further finds that the collection, storage, procurement, use and sale or other monetization of biometric identifier information is inconsistent with basic privacy expectations." This 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. Democratic Legislator Lawrence Dupre said that he first proposed the legislation to make any retailer using the technology more transparent. "You can reset a password. You cannot reset your face," said Dupre. "Your face is yours. It is not a data point for a corporation to collect and sell. As of today, that practice is banned in Erie County. This law does not ban security cameras. It does not ban loss prevention. The line it draws is between a security camera and a biometric database. A store can record video for security. What
Apr 30, 2026 · via spectrumlocalnews.com
Artificial Intelligence Sun Finance automates ID extraction and fraud detection with generative AI on AWS This post was co-authored with Krišjānis Kočāns, Kaspars Magaznieks, Sergei Kiriasov from Sun Finance Group If you process identity documents at scale—loan applications, account openings, compliance checks—you’ve likely hit the same wall: traditional optical character recognition (OCR) gets you partway there, but extraction errors still push a large share of applications into manual review queues. Add fraud detection to the mix, and the manual workload compounds. Sun Finance, a Latvian fintech founded in 2017, operates as a technology-first online lending marketplace across nine countries. The company processes a new loan request every 0.63 seconds and delivers more than 4 million evaluations monthly. In one of their highest-volume industries, with 80,000 monthly applications for microloans, approximately 60% of applications required manual operator review. Sun Finance partnered with the AWS Generative AI Innovation Center to rebuild the pipeline. Within 35 business days of handover, the solution was live in production. The following timeline shows the full project journey from kickoff to production launch. Sun Finance project timeline from kickoff to production The project moved through four milestones over 107 business days. The AWS Generative AI Innovation Center engagement ran 32 days from kickoff (August 26, 2025) to final presentation (October 9, 2025), followed by 26 days for technical handover (November 14, 2025). Sun Finance then took 35 business days to move the solution into production, including a 14-day production freeze over the holiday period (December 18 – January 7), and went live on January 22, 2026. In this post, we show how Sun Finance used Amazon Bedrock, Amazon Textract, and Amazon Rekognition to build an AI-powered identity verification (IDV) pipeline. The solution improved extraction accuracy from 79.7% to 90.8%, cut per-document costs by 91%, and reduced processing
Apr 30, 2026 · via aws.amazon.com
Navigating Japan's proposed APPI amendments: Key timelines, open issues and action points Exploring the expected timeline of the bill to amend Japan's Act on the Protection of Personal Information, areas where further rules are expected and practical steps for businesses. Contributors: Hiroyuki Tanaka Partner Mori Hamada & Matsumoto Kohei Wachi Counsel Mori Hamada & Matsumoto Japan's Cabinet 7 April approved a bill to amend the Act on the Protection of Personal Information and submitted it to the Diet, where it is expected to be enacted during the current session. These amendments aim to strike a balance. On the one hand, they promote broader use of data — including for artificial intelligence development. On the other hand, they introduce stricter rules to address risks such as biometric tracking, misuse of data and handling children's information. A brief overview of the proposed APPI amendments Promoting appropriate data use — deregulation. The most important change is a new consent exemption for the "Creation of statistical information, etc.," which includes certain AI developments. In certain cases, businesses can collect publicly available sensitive personal information and share personal information and personally referable information with third parties without the consent of the data subject, provided the purpose is limited to statistical use and is subject to strict prohibitions on repurposing or further provision of such data. The general consent requirements are also relaxed for processing that is necessary for the performance of a contract or that clearly does not prejudice the individual's rights. Also, the academic research exception now explicitly covers hospitals and clinics. Risk-based regulatory measures. New rules target "Specific Biometric Personal Information," that is, facial recognition data, requiring notification to, or making the information readily accessible to, the data subject and granting the data subject an unconditional right to demand suspension of use. Contributors:
Apr 30, 2026 · via iapp.org
It’s a biometric world after all: Disney offers facial matching for California park entry Disney has deployed facial biometrics for the majority of entry lanes at Disneyland Resort in Anaheim, California. A notice on the brand’s website says the technology is set up at the entrances to Disneyland Park and Disney California Adventure Park, to facilitate ease of reentry and help cut down on fraud. Using the facial recognition lanes is optional. The biometric access control system captures face biometrics at the entrance and “the image of your face that was saved when you first used the ticket or pass,” then uses software to convert those images into unique numerical values. Matching involves comparing the numerical values. In terms of data retention, “except in cases where data must be maintained for legal or fraud-prevention purposes,” Disney will delete all numerical values within 30 days of creation. The notice does not specify retention policies for the images themselves, or which biometrics providers have been engaged to support the system. Disney will still take your picture if you use the standard entry lanes. “However, these lanes will not utilize biometric technology on your image. Instead, a Cast Member will manually validate your ticket.” Despite the collection of biometric data, “children under the age of 18 may use this service with the consent of a parent or guardian.” Disney has trialled facial recognition at its resorts before, first in 2021 at Walt Disney World in Orlando, Florida, then in 2024 at Disney California Adventure. All of the parks run by its competitor, Universal Orlando, have had facial recognition as an entry option since 2023. Coverage in the Los Angeles Times notes the concern from privacy rights groups and academics. It quotes Ari Waldman, a professor of law at UC Irvine, who says “the
Apr 30, 2026 · via biometricupdate.com
LOS ANGELES -- There's nothing quite as identifiable as a face. Retailers use facial recognition technology to more easily nab shoplifters. Casinos have deployed it to keep card counters away. Even a popular New York City venue allegedly uses it to blackball people its millionaire owner considers adversaries. So, it comes as no surprise to many Disneyland guests that it's now being used at the entrance to the Happiest Place on Earth. "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." Photographs of a guest's face taken at the entrance to Disneyland and 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. Disney officials say the technology helps make entering and reentering the park easier and prevents fraud. But 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 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." Venues over the last decade have increasingly begun to lean on facial recognition to speed up entry and purchases for guests. At Intuit Dome visitors can use "GameFaceID" to more quickly enter the stadium for Los
Apr 30, 2026 · via newstribune.com
SASSA plans to introduce voice recognition systems Ntuthuzelo Nene 30 April 2026 | 14:20Last September, the agency introduced a mandatory biometric system at all offices nationwide to combat fraud and verify the authenticity of recipients. SASSA national and Western Cape officials. Photo: Ntuthuzelo Nene The South African Social Security Agency (SASSA) said that it plans to expand its security features beyond fingerprint and facial recognition systems. Last September, the agency introduced a mandatory biometric system at all offices nationwide to combat fraud and verify the authenticity of recipients. The move followed significant challenges with SASSA payments, including widespread fraud, identity theft, and technical glitches. SASSA’s Executive Manager for Grant Administration, Brenton Van Vrede said the agency is now looking to incorporate voice recognition as part of its efforts to modernise its systems. "Because what we want to do is to create a trust centre where we capture all your biometrics. Noting there is no organisation that has every single biometric of every single person so that still has to be built. Home Affairs remains our foundational database for fingerprints and facials. So home affairs will ultimately, once everyone has replaced their green ID's and got smart ID's, the authoritative source on face and fingerprints." Get the whole picture 💡 Take a look at the topic timeline for all related articles.
Apr 30, 2026 · via ewn.co.za
Community worker Shaun Thompson and privacy campaigner Silkie Carlo, of Big Brother Watch, had been seeking to challenge the Metropolitan Police’s use of live facial recognition (LFR) under the European Convention on Human Rights, claiming the usage breached Articles 8, 10 and 11 of the ECHR. But their claim was rejected by the High Court, which ruled their human rights had not been breached by the technology’s usage, which was ““in accordance with the law”. The ruling comes after the Home Office recently ran a consultation on increasing use of facial recognition systems across the UK. Malcolm Dowden, a privacy expert with Pinsent Masons, said the decision would open the door to wider deployment of the tools by the authorities. “This case had been viewed as the first major challenge to deployment based on APP guidance on using facial recognition,” he said. “Its rejection is likely to fuel increased use of automated facial recognition, not only in policing but also - following the recent Home Office consultation - in areas such as border and immigration control.” Thompson had previously been misidentified by facial recognition systems in London, which had led to him being detained and questioned by police after it incorrectly identified him as his brother, who was on bail at the time. Lawyers argued that the use of the technology was a breach of privacy and pointed to a similar case involving South Wales Police in 2020 where the Court of Appeal had found the deployment of facial recognition systems had been unlawful, and was done so without adequate legal safeguards, leaving discretion to individual officers. Since that ruling in 2020 the College of Policing has issued authorised professional practice (APP) guidance on the deployment of live facial recognition technology which ensures that police forces address issues relating to
Apr 30, 2026 · via pinsentmasons.com
Abstract Video-based or image-based human activity recognition (HAR) via machine learning algorithms helps track, detect, and categorize users’ daily activities. It is usually formulated as specific research problems, such as fall detection, gait recognition, posture recognition, and gesture recognition. In practice, HAR is extended to a more generic group activity recognition (GAR) problem, focusing on multiple user (group) activities for various purposes, such as the recognition of social activities, autonomous driving, surveillance, and pedestrian crossing management. In this research, the formulation of the GAR problem aims to enhance the performance of the GAR model by tackling the research limitations in three aspects: (i) image quality may not always be guaranteed, resulting bias and inaccurate models; (ii) the working environments, in both indoor and outdoor, of the GAR model are dynamic, such as varying light conditions; and (iii) the number of people in the covered area may vary and the number of class labels for activities is large, leading to an extensive computing time to train a complex GAR model architecture. Therefore, a dynamic light image enhancement generative adversarial network has been proposed to enhance the image quality significantly. A multi-input image-enhanced generative adversarial network (MIIEGAN) has been proposed for generating high-quality additional training images, and a guided asymmetric depthwise separable convolution (GA-DSC) contributed to the model complexity and performance. These algorithms were evaluated in dynamic light environments to investigate their robustness and performance in varying light conditions that meet the nature of GAR in indoor and outdoor environments. Two benchmark datasets (The Volleyball Dataset and The Collective Dataset) were selected for the performance evaluation and comparison. Five backbones (AlexNet, VGG-16, Inception-v3, ResNet-50, and EfficientNetV2) were chosen as deep learning models for analysis. Nine existing methods were compared. Five variants of GANs and five variants of convolutional techniques were compared in
Apr 30, 2026 · via nature.com
DeepSeek "Open Eye" Sets the AI Community Ablaze: I Tested Its Capabilities with 12 Tricky Images to Find Its Limits Five days after DeepSeek delivered a powerful punch with V4, completely detonating the tech circle, Chen Xiaokang, a researcher in charge of multimodality within DeepSeek, posted the following on X and attached the text: Now, we see you. (Image source: Lei Technology) Yes, it means exactly what it says. While everyone was still amazed by the price and coding ability of V4, DeepSeek suddenly started testing the image recognition mode. The multimodal ability that the whole network had been discussing for a whole year has finally been implemented. The speed of this update really makes people wonder if Liang Wenfeng locked the development team in the computer room overnight to avoid being made into meme images of being irresponsible by netizens. It should be noted that this test is not a full - scale test but a small - scale gray - scale test. Only some users can see it in the official DeepSeek App or web version. At this time, in addition to the original quick mode and expert mode above the input field, there will also be a new image recognition mode button, marked with "Image understanding function is in internal testing". (Image source: Lei Technology) Unfortunately, none of my colleagues were selected for the gray - scale test. The number of people selected by the DeepSeek official was actually zero! Fortunately, I actually became the chosen one in ten thousand. Since it's such a coincidence, I'd feel a bit guilty if I didn't test it for everyone. This time, I carefully selected 12 pictures to let everyone see what DeepSeek can actually "see". Strong understanding ability, knowledge base needs to be updated Without further ado, let's start
Apr 30, 2026 · via eu.36kr.com
DietPal is an AI-powered nutrition tracking application designed to simplify calorie and fitness monitoring through conversational input and image recognition. Users can log meals, workouts, and body weight by interacting with a chat-based interface or by uploading food photos for automatic identification. The system processes this data to provide calorie estimates, nutritional breakdowns, and progress tracking over time. It also generates personalized diet recommendations based on user inputs and goals. Visual analytics, such as charts and graphs, are used to present trends in dietary habits and fitness progress. The platform is typically used by individuals aiming to manage weight, improve nutrition, or maintain structured health routines. DietPal reflects a broader trend in health technology that integrates artificial intelligence to reduce manual tracking effort and increase accessibility. Its primary function is to streamline dietary monitoring and support data-driven health and fitness decisions. AI Nutrition Tools DietPal AI Calorie Tracker Lets Users Log Meals And Track Nutrition Easily Trend Themes - Conversational Nutrition Interfaces — Natural language chat interfaces that allow logging and guidance through dialogue enable more intuitive and lower-friction user engagement with nutrition data. - Image-based Food Recognition — Photo-to-nutrient analysis that automatically identifies dishes and estimates portions opens possibilities for passive dietary tracking and richer food databases. - Personalized Diet Recommendations — AI-generated meal plans and nutrient targets tailored to individual goals and behaviors create opportunities for hyper-tailored nutrition strategies tied to outcomes. Industry Implications - Consumer Health Apps — Mobile wellness platforms that integrate AI tracking and analytics can shift user expectations toward continuous, personalized health feedback. - Fitness and Wellness Platforms — Integrated workout and nutrition ecosystems that combine conversational logging with progress visualization enable cohesive behavior-change offerings. - Food and Beverage Retail — Retailers and meal providers leveraging image recognition and personalized recommendations could transform product
Apr 30, 2026 · via trendhunter.com
“Suddenly the definition of what people meant when they were talking about AI changed underneath us,” Raiche said. “We went from talking about AI as statistical models for computer vision and image recognition, which … when we started our code of ethics and all that work, that was the world we were living in. Then suddenly we were in a world of generative AI.” Since then, Raiche and Vermont have established themselves as national trailblazers on AI. That code of ethics — which compares AI to power tools, instruments to be used by skilled operators to more effectively carry out tasks — has been adopted by many state and local governments across the country. Raiche has presented to other states setting up AI councils about his own state’s group, on which he sits. Raiche’s current title is chief data and AI officer, a role that recognizes the vital role data plays in enabling the state to work with AI. Often, that boils down to standardizing and streamlining procedures. “The prerequisite to actually doing the shiny AI work is to go through and update processes and to develop some consistency,” Raiche said. “It’s really business process engineering work that happens first, which will result in improved data quality, which unlocks the ability to leverage AI.” It’s that collaborative nature, with peers both in and outside the state, that Raiche said brings a positivity to his work. “We have folks who lean in to, ‘What could this look like?’ and who take the opportunity to learn,” he said. “And those people really encourage me when they trust that their leadership has their best interests and the best interests of Vermonters in mind, and then they lean in and learn something new and take a risk and come out better for it on
Apr 30, 2026 · via govtech.com
Facial recognition is now part of the security process at many U.S. airports, but travelers are not required to use it. At Transportation Security Administration (TSA) checkpoints, the technology usually appears at the identity verification podium before passengers reach the bag screening area. Instead of only handing over an ID for a TSA officer to inspect, a traveler may be asked to place a driver’s license, passport, or other acceptable identification into a Credential Authentication Technology device. At some checkpoints, that device can also take a live photo of the traveler. TSA says the system compares that live image with the photo on the traveler’s ID to help confirm that the person at the checkpoint matches the document presented. TSA describes this as facial comparison technology used for identity verification, and says the images are not used for law enforcement or surveillance. Still, many travelers may not realize they have a choice. According to TSA, passengers may opt out of facial recognition and request an alternative identity verification process. What The Facial Recognition Process Looks Like At checkpoints using TSA’s newer identity verification machines, the process usually starts when a traveler reaches the document checking station. The traveler presents an ID, and the machine checks the document’s information. If facial comparison is active and the traveler does not opt out, the device takes a live image at the podium. The system then compares that live photo with the image on the traveler’s government-issued ID. A TSA officer can review the result before allowing the traveler to continue to standard security screening. The Privacy and Civil Liberties Oversight Board’s 2025 report describes TSA’s checkpoint facial recognition system as a one-to-one matching system. That means it compares the live photo to the ID photo the traveler provided, instead of searching the person’s
Apr 29, 2026 · via travelnoire.com
In the last days of 2025, among tech commenters in rarefied corners of X, a new consensus emerged: AGI was here. By AGI, they meant artificial general intelligence—a term of art usually taken to denote computer programs that can match or exceed human capabilities at most economically valuable tasks. The immediate cause for the excitement was a series of updates to Anthropic’s AI coding tool, Claude Code, allowing it to complete complex programming tasks far more reliably, and with far less human supervision, than before. Taken merely as another productivity boost for software engineers, this would not be especially noteworthy. But understood as a stepwise improvement in a general system capable of performing almost any kind of computer-mediated work, it looked like a watershed: the automation not merely of coding as a specialized skill but potentially of any work performed with a computer. An Anthropic engineer posted that Claude Code was, itself, written almost entirely by a previous version of Claude Code. Its human overseers focused on “foundational architectural and product decisions,” while the AI implemented the solutions. Even the most in-the-weeds engineers on his team hardly wrote their own code anymore; they instead directed a team of agents. The long-foretold transformation of work seemed to have arrived. So, too, perhaps, had the man–computer symbiosis first described over 60 years ago by J. C. R. Licklider. “I have to constantly model the mind of [AI agents] living inside my laptop, and in doing so I become more like them. . . . I think in context windows. I become a cyborg, a hive mind of human and clauds,” wrote one anonymous tech poster on X. Finally, a reason to check your email. Sign up for our free newsletter today. And yet as excitement about the singularity grew in and around
Apr 29, 2026 · via city-journal.org
Abstract Background: Cataracts are an eye condition characterized by high prevalence and blindness-inducing potential, and effective approaches are required for their early diagnosis, underscoring the clinical significance of this study. Objective: This study aims to evaluate the performance of deep learning (DL) in cataract diagnosis and assess its potential as an effective tool for automated diagnosis, and compare the diagnostic accuracy of DL versus both machine learning and human experts. Methods: A systematic search was conducted in Web of Science, Embase, IEEE Xplore, PubMed, and Cochrane Library until April 1, 2025, for studies on image-based DL for cataract detection or clinical subtype classification. The included studies were assessed for the risk of bias (RoB) using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Bivariate mixed effects models were used for data analyses, and publication bias was assessed by Deeks’ funnel plots. Results: Sixty-three studies were finally included. The quality assessment indicated a high or unclear RoB in the patient selection (34 studies) and index test (44 studies) domains. Meanwhile, in the reference standard domain, the risk of bias was high or unclear in only 2 studies. Image-based DL achieved a sensitivity of 96% (95% CI 0.95‐0.97) and a specificity of 98% (0.96‐0.98) for cataract detection, with an area under the ROC curve (AUC) of 0.99 (0.98‐1.00). For cataract classification, the sensitivity and specificity of image-based DL were 94% (0.93‐0.96) and 97% (0.96‐0.98), respectively, with an AUC of 0.99 (0.98‐0.99). Despite the strong overall performance, the model’s generalization capability was challenged by its lower performance observed on independent external datasets (detection: sensitivity 87%, specificity 93%; classification: sensitivity 89%, specificity 90%), potentially attributable to domain shift between the training and validation data. Conclusions: Image-based DL has demonstrated high precision in the detection and classification of cataracts, showing potential advantages over traditional machine learning
Apr 29, 2026 · via jmir.org
New Orleans Police Continuing to Use Live Face Recognition Despite City Law Subscribe to the Free Future Newsletter Free Future home A troubling New Orleans face recognition program revealed by a Washington Post investigation last year continues to operate despite violating a city law and a claim that it has been paused, and is being used in more problematic ways than first reported, according to emails obtained through an activist’s public records requests. If allowed to stand, this one-of-a-kind program would represent an alarming new expansion of how face recognition is used in America. Face recognition is used by a number of police agencies around the nation, but almost always to discover the identity of an alleged perpetrator who appears in a photo. New Orleans police officers, however, have used — and appear to be continuing to use —live face recognition through an organization called Project NOLA. That means that they can do: - Real time monitoring. The systems operators can set the system to issue instant alerts when someone on a watch list appears on some of the system’s 5,000 camera system (the newer ones, which have face recognition functionality embedded within them). - Search. The operators can also enter a name or image and pull up the video of every time the person appears in any of the recordings the system has made (Project NOLA says it retains them for 30 days). - Tracking. The retroactive construction of a person’s movements across the city over time, which can be highly revealing of how they live their life. - Relationship identification. Such a system also enables even more invasive social network analysis by tracking a person’s repeated co-appearances in a video with another person. There has been no reporting that Project NOLA is doing this in automated fashion, but
Apr 29, 2026 · via aclu.org
Announcing a new publication from Opto-Electronic Advances; DOI 10.29026/oea.2026.250263 SHANNON, CLARE, IRELAND, April 29, 2026 /EINPresswire.com/ — Announcing a new publication from Opto-Electronic Advances; DOI 10.29026/oea.2026.250263 For centuries, the manipulation of light has relied on bulky, heavy components like glass lenses and prisms. From the camera in a smartphone to the microscopes in a laboratory, traditional optics are constrained by physical laws that dictate the size and thickness. However, a revolution is underway with the advent of “metasurfaces”. They are ultra-thin, flat optical devices composed of millions of sub-wavelength structures that can bend, focus, and filter light at will, in ways that natural materials cannot. This has the potential to shrink bulky cameras down to the thickness of a sheet of paper. Yet, designing these metasurfaces is a task of immense complexity. A single device may contain millions of nano-pillars, each requiring precise engineering and fabrication. Navigating this infinite design space using traditional human intuition or standard computer simulations is a significant bottleneck. This review article explores how artificial intelligence (AI) is shattering that bottleneck. Just as AI has transformed language processing and image recognition, it is now reshaping the field of optics, giving rise to “AI-Assisted Metaphotonics.” Deep learning algorithms are proving to be the perfect partner for nanophotonics. Where a human engineer might take weeks to simulate a single design, AI-powered “surrogate models” can predict how light will interact with a nanostructure in milliseconds. More impressively, AI enables “inverse design.” Instead of an engineer guessing a structure and checking if it works, they can simply tell the AI the desired optical property, such as a specific color or focal length, and the AI generates the exact complex geometry required to achieve it. But the synergy goes beyond just design. The review highlights how AI is being integrated into
Apr 29, 2026 · via desmoinesregister.com
In summary: - PCWorld tested Windows Hello facial recognition security by attempting to fool the system with photos, finding it effectively resisted all spoofing attempts including high-resolution iPad images and printed photos with eye holes. - Windows Hello uses IR cameras and 3D depth mapping technology to distinguish real faces from flat images, storing facial landmark data locally rather than actual photos for enhanced privacy and security. - The system proves significantly more secure than older facial recognition methods and traditional PINs or passwords, requiring sophisticated 3D facial replicas to potentially bypass its advanced biometric protection. Long ago, I had an Android phone with an early facial recognition sign-in feature… and someone could unlock my phone just by holding up a photo of me. Yeah, it was bad. Fast forward to 2025 and we have Windows Hello facial recognition sign-ins for PCs. Microsoft talks a big game about how secure it is, that Windows Hello can’t be easily tricked, that it’s better than a traditional PIN or password, and that it’s as secure as Apple’s Face ID. But is it really? I ran an experiment and tried to fool it. Here’s what happened when I put facial recognition to the test on my PC. How I tried to fool Windows Hello If someone wanted to fool facial recognition biometrics, they’d probably do it using a photo of your face. So that’s just what I did—I took a photo of myself (available online), put it on an iPad, and held it up in front of my face. My Windows Hello webcam wasn’t fooled for a second. In fact, Windows Hello doesn’t even see flat pictures as faces! While the Camera app on Windows does register it as a face, Windows Hello knows better. Despite holding up a high-resolution image of my
Apr 29, 2026 · via pcworld.com
Disney has rolled out facial recognition software at its theme parks in a fraud crackdown that has led to warnings from privacy experts. Pictures taken at certain entrances to Disneyland and Disney California Adventure in Anaheim are compared to images taken when a guest first used a ticket or annual pass, the company said, arguing that the technology can be used to stop fraud or potentially to prevent the misuse of annual passes. Facial recognition is optional and not in use at all entrances, Disney said. The data is deleted after 30 days except in cases where it “must be maintained for legal or fraud-prevention purposes”, according to the company’s website. Yet some experts have raised privacy concerns as the use of facial recognition software spreads throughout society. “The normalisation of facial surveillance is really problematic,” Ari Waldman, a professor of law at UC Irvine, told the Los Angeles Times. “We can’t go around life hiding our faces, so this isn’t just 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.” Major sporting and entertainment venues have been using facial recognition software for what they say are security reasons. In Major League Baseball for example, many franchises allow fans to partake in “Go Ahead Entry”, which enables them to enter without a ticket as long as their faces are scanned. Madison Square Garden, the New York venue owned by the billionaire James Dolan, has been criticised for allegedly using the technology to keep track of critics of the businessman. The American Civil Liberties Union is among the groups that have previously raised the alarm about how prevalent the software is becoming in public life. Most fans at Disneyland recently paid little attention to the signs alerting
Apr 29, 2026 · via thetimes.com