Department of Injustice Jalil Richardson of North Carolina is free after spending over 50 days in jail after being wrongfully arrested for a crime he did not commit. According to Action News Jax, Richardson was initially accused of stealing a vehicle in Jacksonville, Florida, after police fed surveillance video from a private business into their AI-integrated facial recognition system. The system then identified Richardson with what it said was an 85 percent facial recognition match, the Florida attorney’s office told Jax. Paired with two “eyewitness” accounts, it was enough to establish probable cause against Richardson, even though he’d been clocked into his job hundreds of miles away when the crime took place. After being arrested and made to spend nearly two months in custody, Richardson and his lawyers were finally able to establish his alibi in court, forcing prosecutors to drop the case — an infuriating miscarriage of justice, and quite possibly a sign of things to come as cops across the country embrace flawed facial recognition systems as a shortcut to investigating crimes. “There was no proper investigation done to even reach out to me or to see if I was even in Florida,” Richardson told Jax. “And I sat in there for over 50 days in the most worst jail ever.” Wrongful arrests based on AI facial recognition software are becoming something of a pattern with the Jacksonville Sheriff’s Office. Their first victim was Robert Dillon, a “93 percent match” who was wrongfully accused of attempting to lure and kidnap a 12-year-old child. Like Richardson, Dillon was a world away at the time — a five hour drive on the other side of the state. According to privacy litigation director for the Electronic Frontier Foundation Adam Schwartz, it’s the 14th known case of a wrongful arrest due to
Jun 10, 2026 · via futurism.com
AI software identifies robbery suspect ABILENE, Texas — A Taylor County man has been identified as the suspect in a series of armed robberies at convenience stores in Abilene earlier this month, according to a probable cause affidavit filed by law enforcement. Jacob Arriaga, 32, is accused of committing three robberies between June 2 and June 8 at convenience stores across the city while allegedly displaying or implying possession of a handgun and demanding cash from store clerks. RELATED| Automated license plate readers speed up Texas crime-fighting, police association says According to the affidavit, the first robbery occurred around 5:09 a.m. June 2 at the DK Convenience Store on South 14th Street. Investigators said a man entered the store under the pretense of needing change before brandishing what appeared to be a black 1911-style handgun and demanding money from the cash register. The suspect fled with cash and left in a newer blue four-door SUV. A second robbery was reported around 3:30 a.m. June 8 at another DK Convenience Store on East Highway 80. The affidavit states the suspect lifted his shirt to reveal what appeared to be a dark-colored handgun before demanding money. The clerk handed over approximately $125. RELATED| Five alleged gang members arrested after Abilene Police investigation About 10 minutes later, police responded to a robbery at the Allsups Convenience Store on North Judge Ely Boulevard. Investigators said the suspect waited outside until customers left before entering and asking for cigarettes. When the clerk turned to retrieve them, the suspect allegedly displayed a handgun and demanded cash. The clerk surrendered about $148. In each incident, witnesses described the suspect as a white male, about 5-foot-8, heavyset, with short brown hair and a full brown beard. Surveillance footage also showed the suspect wearing similar clothing during the robberies,
Jun 10, 2026 · via ktxs.com
MOSIP accredits Fime for biometric device testing French digital ID firm Fime has secured accreditation from MOSIP allowing it to conduct accredited biometric device testing for MOSIP-based digital identity programs. According to the company, the accreditation allows its laboratory to provide “independent, internationally recognized validation that biometric devices meet industry standards and MOSIP requirements.” National digital identity programs are sovereign infrastructure as vital as roads, hospitals, or schools, MOSIP’s Vice President for Partner Ecosystems, Sanjith Sundaram remarked after the accreditation. He added: “By accrediting Fime, MOSIP strengthens the ecosystem with independent testing capabilities that ensure biometric devices meet the highest standards of performance, security, and inclusivity.” Governments worldwide continue expanding national digital identity programs, with trust, inclusion and interoperability being among their priority considerations. Trust in digital ID lies largely on the quality of biometric data collected from subjects during enrollment, and this can only be guaranteed if devices deployed meet certain standards and specifications. Fime says the accreditation allows it to support governments in selecting biometric devices for MOSIP-based digital identity deployments during procurement. The accreditation also enables the company to test devices across population groups and deployment environments while supporting vendors seeking participation in the growing MOSIP ecosystem. Commenting on the accreditation, the SVP for Services at Fime, Noël Catherine, emphasized that biometric image quality is a key contributor to a trusted digital ID program. “With this accreditation, we support governments and partners with the validation needed to reduce risk, accelerate deployment, and deliver inclusive identity programs at scale,” she said. Fime’s laboratory is reputed for biometric testing across areas including matching performance, presentation attack detection, biometric data injection attack detection, and bias evaluation in biometric systems for populations of different age groups and characteristics. MOSIP’s growing role in national digital ID Biometric device testing for MOSIP falls
Jun 10, 2026 · via biometricupdate.com
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Jun 9, 2026 · via youtube.com
Watch NASA's new experimental jet break the sound barrier for the first time NASA's X-59 jet has broken the sound barrier for the first time, a major milestone in the effort to build a quiet supersonic aircraft for civilians. The sleek, needle-nosed airplane exceeded the speed of sound for the first time on Friday, June 5. NASA test pilot Jim "Clue" Less took off in the plane at 11:08 a.m. PT and landed at Edwards Air Force Base in California about 81 minutes later. At the aircraft's top speed, it went Mach 1.1 — about 713 mph — at an altitude of 43,400 feet. The X-59 is experimental, part of the so-called QUESST mission to transform passenger air travel over land. Because existing supersonic aircraft produce loud sonic booms at high speed, the U.S. government bans routine supersonic flights over populated areas. But the X-59, designed by NASA and its contractor Lockheed Martin, is expected to tame the boom into a mere thump. You May Also Like NASA captured the achievement from the vantage point of a chase plane, which kept pace with the speedy experimental jet to monitor the test. You can watch the historic moment when it clocked supersonic speed in the video below. "X-59 goes through the number!" NASA administrator Jared Isaacman said on X. "We are rebuilding our X-plane portfolio and getting NASA back in the business of radical airframe and engine flight test!" An X‑plane is a U.S. aircraft designed to test new flight technologies and ideas. The goal of the X-59 is to provide regulators and the airline industry with the evidence needed to reconsider restrictions on supersonic aircraft. Most people think of NASA as the gateway to space, but the organization is first and foremost the nation's civil aeronautics agency (quite literally, the
Jun 9, 2026 · via mashable.com
By Raji Natarajan, Special to Rice News Picture a busy street during rush hour with vehicles zipping past and pedestrians rushing by. While humans can effortlessly perceive and interpret such dynamic scenes in real time, current 3D imaging technologies often struggle to create accurate representations of rapidly changing environments. A new study published in Nature Communications by researchers at Rice University and the University of Arizona introduces an innovative approach that could help machines see the world more clearly. Ashok Veeraraghavan, chair of the Department of Electrical and Computer Engineering in Rice’s George R. Brown School of Engineering and Computing, and Aniket Dashpute, a graduate student in his lab, in collaboration with associate professor Florian Willomitzer and his team at the University of Arizona’s Wyant College of Optical Sciences, developed a two-step computational imaging method that uses a laser and a high-speed camera to capture complex scenes in three dimensions with exceptional speed and accuracy. The key innovation lies in the system’s ability to transform ordinary matte surfaces into virtual screens. By using surrounding walls, furniture, clothing and other nonreflective surfaces as part of the imaging process, the technique enables accurate 3D reconstruction of scenes containing both matte and reflective objects, a long-standing challenge in computer vision. The advance could improve machine vision for applications ranging from industrial inspection and facial recognition to human sensing and autonomous vehicles. Most 3D imaging systems rely on structured light, which projects patterns onto a scene and measures how those patterns deform across object surfaces to create depth maps. While widely used, these systems can struggle with motion, challenging lighting conditions and scenes containing both matte and reflective materials. In mixed-reflectance environments, light bouncing between surfaces can distort measurements and reduce image quality. “To address these challenges, we leveraged a well-established technique in computer
Jun 9, 2026 · via news.rice.edu
Figures Abstract Accurate classification of sea turtle species is crucial for ecological monitoring and conservation, yet traditional visual classification methods remain limited by underwater imaging challenges such as occlusions, poor lighting, and background noise. To address these limitations, we propose an enhanced deep learning-based classification framework that integrates both color and structural features to improve the robustness of species recognition in complex marine environments. Building upon the ResNet-50 backbone, we introduce a four-channel input tensor comprising RGB data and Sobel-filtered edge maps, capturing both semantic and morphological information. Two novel fusion modules, LiteAFNet and AlphaBlendNet, are designed to integrate these features effectively. LiteAFNet leverages a lightweight attention mechanism to highlight discriminative regions, while AlphaBlendNet adaptively balances RGB and edge cues based on spatial context. Experimental results demonstrate significant improvements in classification performance across all evaluation metrics. Specifically, AlphaBlendNet achieves the highest precision (0.84), recall (0.88), F1-score (0.86), and mean average precision (mAP) of 87.2%, outperforming both the baseline fusion and LiteAFNet configurations. These results indicate that integrating color histograms with structural edge features enhances the model’s ability to distinguish between species with similar visual traits. This framework offers a scalable, accurate, and automated solution for underwater species classification and holds potential for broader application in marine biodiversity monitoring. Citation: Chaisiriprasert P, Deearom A (2026) A deep learning-based fusion framework for robust fine-grained classification of sea turtles in support of marine biodiversity. PLoS One 21(6): e0344942. https://doi.org/10.1371/journal.pone.0344942 Editor: Yaseen Ahmed Al-Mulla, Sultan Qaboos University, OMAN Received: August 13, 2025; Accepted: February 26, 2026; Published: June 9, 2026 Copyright: © 2026 Chaisiriprasert, Deearom. 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: DOIs 10.6084/m9.figshare.30452567 URLs
Jun 9, 2026 · via journals.plos.org
TL;DR Meta stripped NameTag facial recognition code from its AI app one day after WIRED exposed it on 50 million phones. Meta says no decision has been made. The NameTag system, which converted faces captured by Ray-Ban smart glasses into biometric signatures and stored unrecognised faces locally, had been embedded in the Meta AI app since January despite the company publicly saying it had made no final decision about facial recognition Meta stripped NameTag facial recognition code from its AI app one day after WIRED exposed it on 50 million phones. Meta says no decision has been made. Meta removed nearly all traces of an unreleased facial recognition system from its smart glasses companion app on Friday, one day after WIRED reported that the software had been quietly embedded in an app installed on more than 50 million phones. The feature, which Meta internally called NameTag, was designed to convert faces captured by the company’s Ray-Ban smart glasses into unique biometric signatures and compare them against a database stored on the user’s device. WIRED also found that faces the system failed to recognise were cropped, indexed, and stored locally for future processing. Andy Stone, Meta’s vice president of communications, told WIRED on Monday that the feature is “purely exploratory,” adding that no final decision has been made on what to do with it. That characterisation sits uneasily with the evidence WIRED documented. The version of Meta AI published the day of WIRED’s Thursday report contained several code libraries explicitly named for face recognition, a process for running the NameTag recognition pipeline, and a “Person recognised” alert the app would have shown if someone were identified. Friday’s release stripped all of it out, along with a folder where the app would have stored the cropped images and biometric signatures of unrecognised
Jun 9, 2026 · via thenextweb.com
Newswise — NEW YORK, 09 June 2026 – The IEEE Computer Society (CS) and the Computer Vision Foundation (CVF) announced the award-winning papers from the 2026 Conference on Computer Vision and Pattern Recognition (CVPR), recognizing outstanding achievements in computer vision. Best Paper Awards Following a rigorous review process that resulted in 4,089 accepted papers from 16,092 submissions, the CVPR 2026 Awards Selection Committee selected the following two papers for top honors at this year’s conference: CVPR 2026 Best Paper - Efficiently Reconstructing Dynamic Scenes One D4RT at a Time, Authors: Chuhan Zhang; Guillaume Le Moing; Skanda Koppula; Ignacio Rocco; Liliane Momeni; Junyu Xie; Shuyang Sun; Rahul Sukthankar; Joëlle K. Barral; Raia Hadsell; Zoubin Ghahramani; Andrew Zisserman; Junlin Zhang; Mehdi S. M. Sajjadi - A team from Google DeepMind, the University College London, and the University of Oxford developed D4RT, a network that can reconstruct the geometry and motion of dynamic 4D scenes from video. Using a unified transformer-based architecture, the model estimates depth, spatio-temporal correspondence, and full camera parameters, allowing for the independent and efficient probing of a 3D position of any point in space and time. By simplifying what has traditionally been a computationally intensive process, D4RT provides a lightweight and highly scalable method that enables remarkably efficient training and inference. CVPR 2026 Best Student Paper - Native and Compact Structured Latents for 3D Generation, Authors: Jianfeng Xiang; Xiaoxue Chen; Sicheng Xu; Ruicheng Wang; Zelong Lv; Yu Deng; Hongyuan Zhu; Yue Dong; Hao Zhao; Nicholas Jing Yuan; Jiaolong Yang - A team from Tsinghua University, Microsoft Research, the University of Science and Technology of China, and Microsoft AI developed a new approach to 3D generative modeling that significantly improves the quality and realism of AI-generated 3D assets. The research is centered around O-Voxel, a novel representation that can accurately
Jun 9, 2026 · via newswise.com
SPARC AI Demonstrates 43km Target Acquisition in GPS-Denied Test Event summary - SPARC AI completed a 43km target acquisition test over open water in Port Phillip Bay, Australia, at a drone height of 115m. - The test demonstrated capability comparable to the Strait of Hormuz, a critical maritime chokepoint. - SPARC AI integrated image recognition into its Overwatch drone controller application. - Overwatch now combines targets from multiple drones and manufacturers into a single operating map. - Next phase will introduce multi-drone deployment and swarm capabilities in GPS-denied environments. The big picture SPARC AI's successful long-range test and image recognition integration position Overwatch as a critical software layer for defense drones, particularly in GPS-denied environments. The ability to operate across multiple manufacturers expands its addressable market, challenging traditional proprietary drone platforms. The next phase of development, focusing on multi-drone coordination, could further solidify its competitive edge in autonomous defense systems. What we're watching - Market Expansion - Whether SPARC AI can sustain its position as a premium software layer across diverse drone manufacturers. - Technological Integration - The pace at which multi-drone deployment and swarm capabilities will be adopted by defense partners. - Geopolitical Demand - How the Strait of Hormuz comparison will influence adoption in contested maritime environments. Related topics
Jun 9, 2026 · via briefglance.com
Police use of artificial intelligence (AI): factsheet (accessible) Published 9 June 2026 What is the government’s overall policy position on police use of AI? - In the government’s AI Opportunities Action Plan, the Prime Minister set out his intention to maximise the potential for AI to make the public sector more efficient and effective and deliver the government’s Plan for Change. - To date, most AI activity pursued by police forces in England and Wales has been undertaken by Chief Officers using their core funding and accountable to their elected Police and Crime Commissioners. - Since the 2024 election this has been supplemented by over £50m of direct grant funding from the Home Office for specific projects, such as to fund new Live Facial Recognition vans. - In the Police Reform White Paper, the government announced a further £115m for police adoption of AI and automation which covers a range of projects such as creating a new National Centre for AI in Policing (“PoliceAI”). The aim of these projects is to rapidly equip policing with high quality AI that can make the biggest difference to public safety outcomes in local communities. - Where police forces use AI, it should be deployed responsibly. That means deployments must be lawful, ethical, transparent and based on a robust assessment of algorithms before use. It should be clear who is accountable for the AI’s performance and the use of its output by police personnel, and there should be clear operating procedures in place that set out what appropriate usage looks like. These principles are critical for building and maintaining public consent for the use of this technology. - To assist the police in using AI responsibly, the government has funded the National Police Chief’s Council to develop guidance, which is published on the College
Jun 9, 2026 · via gov.uk
The World Through AI Artificial intelligence (AI) is becoming an increasingly pervasive part of everyday life. AI technologies are fundamentally transforming the ways in which images are created, edited, distributed, described and perceived. From 11 June to 20 September 2026, Schirn Kunsthalle Frankfurt presents its major summer exhibition The World Through AI, exploring the profound impact of artificial intelligence on visual culture and contemporary artistic practice. The exhibition brings together artworks that examine the cognitive, psychological, political and ecological dimensions of AI. The World Through AI is organised by the Jeu de Paume in Paris in collaboration with Schirn Kunsthalle Frankfurt and is curated by Antonio Somaini and Katharina Dohm. A Walk Through the Exhibition Featuring around 40 works by international artists, the exhibition examines the social and cultural impact of artificial intelligence. Topics include machine vision, facial recognition, resource consumption, memory, future imaginaries and AI-generated propaganda. The exhibition is divided into 17 thematic sections. Historical “time capsules” provide insights into the contexts of data storage, image processing and the history of the former Dondorf printing works, which now serves as the exhibition venue. Between Machine Vision and AI Slop The exhibition begins in Hall 1 with the technological foundations of artificial intelligence, highlighting the material and ecological conditions of digital infrastructures, machine vision and the often invisible human labour behind AI systems. Works such as Mechanical Kurds by Hito Steyerl and xhairymutantx by Holly Herndon and Mat Dryhurst explore different forms of AI production and collective practices. Hall 2 focuses on the social and political implications of AI, particularly questions surrounding colonial image archives, AI-generated propaganda and shifting visual cultures. The installation Holy Slop! A Generative Atlas of Slopaganda in Palestine by Occitane Lacurie and Barnabé Sauvage, alongside works by Nouf Aljowaysir and Nora Al-Badri, investigates how AI can
Jun 9, 2026 · via german-design-council.de
There’s a particular weight to memory when you’ve lived through a time that others now only reference in shorthand. I don’t mean nostalgia. I mean the physical act of remembering who is missing. In the 1980s and early 1990s, as AIDS moved through my community with a speed and indifference that still feels impossible to explain, I had address books that became, over time, records of absence. Names crossed out. Numbers that no longer rang. Whole clusters of friends and colleagues gone. Not abstractly, not statistically — specifically. People with voices, habits, jokes, plans. People who should have had the chance to grow older. They didn’t. At the same time, I was an undergraduate in marine biology, expected to keep pace — labs, exams, problem sets — as if the world were intact. Animal physiology, genetics, statistics, organic chemistry. Show up. Perform. Pass. All while a plague burned through my community with terrifying precision. There was no accommodation for grief. No pause. No recognition that anything unusual was happening. The expectation was continuity — business as usual — no matter what was being lost. And while that was happening, the federal government — under Ronald Reagan — withheld urgency in a way that still feels difficult to describe without anger. Years passed before the crisis was even named at the highest level. The silence was ambient, structural. It told us exactly how much our lives were worth in the hierarchy of concern. So we filled the silence ourselves. We marched. We organized. We protested in the streets and in front of federal buildings and in hospital wards. I remember the lines of police in riot gear, the pressure of bodies pushing forward, the stinging waft of tear gas, the sound of voices refusing to be contained. I remember the fear
Jun 9, 2026 · via therevelator.org
Meta’s smart glasses are once again at the center of a privacy debate due to face recognition. WIRED reports that Meta had quietly embedded unreleased face-recognition code, internally called “NameTag,” into its Meta AI companion app, which powers the company’s smart glasses. The code was not active, but its presence in an app installed on more than 50 million devices raised immediate concerns about how quickly using smart glasses could slide into biometric surveillance. Face recognition in glasses, even if disabled or unreleased, is especially sensitive because it can identify people at a distance, in real time, and without their consent. Many organizations have warned that this technology could be misused by stalkers, abusers, and others who want to identify people in public without drawing attention. Gizmodo reports on a proposed Pennsylvania bill that would require smart glasses and similar wearable recording devices to include a visible indicator light when they are capturing audio or video. The bill would also prohibit users from disabling that indicator, a move clearly aimed at reducing covert recording in public spaces. Most smart glasses already include such an indicator, but reporters noted that some users have been paying others to have them removed or disabled. The proposal is interesting because it tries to solve a hardware-level trust problem with a visible signal. But a visible light only helps if it is both mandatory and difficult to bypass, and history suggests that any visible privacy safeguard becomes a target for tampering when the incentives are high enough. These two stories are really about the same issue: smart glasses are normalizing the use of always-on cameras, microphones, and AI features in a form that is much easier to conceal than a phone. That creates an unwanted privacy problem for people around the wearer. Smart glasses are
Jun 9, 2026 · via malwarebytes.com
Abstract Facial emotion recognition (FER) is one of the main fields of research in image processing and artificial intelligence with applications in human–machine interaction, behavior analysis, and intelligent vision. The RAF DB dataset, a widely used benchmark dataset for emotion recognition, is used for the training and testing of deep learning models. In real application conditions, the facial image may be subjected to visual noise. Large glasses that occlude the eye region of the face are one example of environmental or artificial noise which distorts the visual information. If frames covering a large area of an expressive part of the face, like the eyes, are recognized as noise by the emotion recognition network this may lead to noticeable performance degradation. Artificial occlusion noise, similar to large glasses, was applied to the eye region of the facial images from the RAF DB dataset. The impact of this type of structural noise on the performance of deep-learning-based facial emotion recognition models is analyzed. Three state-of-the-art architectures (ResNet-50, Vision Transformer (ViT-Base/16) and Swin Transformer Tiny (Swin T)) are tested to determine their robustness to the noisy application conditions. The objective of the study is to determine and suggest possible methods to counter the decrease in recognition performance caused by the occlusion noise. Experimental results indicate that, within our evaluation setup, transformer-based architectures, especially Swin-T, tend to be more robust to the simulated visual noise, which may be useful for the design of facial emotion recognition models intended for more complex conditions. Similar content being viewed by others Funding No funding was received for this work. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Ethical and informed consent This article does not contain any studies with human participants or animals performed by any of the
Jun 9, 2026 · via nature.com
VANCOUVER, British Columbia, June 09, 2026 (GLOBE NEWSWIRE) -- SPARC AI Inc. (the “Company”) (CSE: SPAI) (OTCQB: SPAIF) (Frankfurt: 5OV0) a defence technology company building Overwatch, the GPS denied navigation and target acquisition software platform for drones and autonomous systems, today announced the successful completion of a 43km long-range target acquisition test conducted over open water in Port Phillip Bay, Victoria, Australia. The target recording was done at a drone height of 115m above ground level. The 43km demonstrated span is comparable to, and in some measurements exceeds, the narrowest width of the Strait of Hormuz , one of the world’s most strategically significant maritime chokepoints. The comparison illustrates the scale of contested, GPS-denied maritime environments in which the capability is designed to operate. SPARC AI is also pleased to announce it has integrated image recognition into the SPARC AI drone controller application, adding further capability to its targeting solution. Overwatch brings together targets recorded by multiple drones across different manufacturers and different locations onto a single operating map, where operators can classify and track targets, collaborate, and plan missions in one shared picture. With image recognition now overlaid onto that picture, operators gain richer intelligence and can respond more rapidly across teams. Capabilities of this kind have historically been locked inside expensive, proprietary drone platforms. By delivering them as software across any manufacturer's hardware, the Company believes Overwatch meaningfully expands its addressable market and positions the platform as a premium software layer rather than a single-aircraft feature. Looking ahead, the next phase of Overwatch's development will introduce the ability to deploy multiple drones directly from the platform. The company is developing teaming and swarm capability that it believes will be unique to Overwatch with the ability to deploy and coordinate drones from different manufacturers, operating from different locations, simultaneously
Jun 9, 2026 · via markets.businessinsider.com
Foodstuffs South Island will continue using facial recognition technology in three Christchurch supermarkets and expand it to a fourth store. The news comes after it conducted a trial between October 2025 and January 2026 designed to identify and manage people with a history of serious and harmful in-store behaviour. During the three-month period, there were 531 confirmed matches with people of interest, with no misidentifications or false positives recorded. Kent Mahon, Head of Retail for Foodstuffs South Island, said the results supported the continued use of the technology. “The focus has always been on reducing harm. The trial showed we can do that while keeping accuracy high and respecting customer privacy.” According to Foodstuffs South Island, staff reported that repeat offenders were less likely to return to stores involved in the trial. The company also said incidents involving threatening or harmful behaviour declined during the period, allowing employees to intervene earlier and reduce risks to customers and staff. The three stores involved in the trial, New World St Martins, PAK’nSAVE Papanui and PAK’nSAVE Moorhouse — will continue using the technology. New World Stanmore will also begin using facial recognition. Foodstuffs South Island said there had been interest from other stores dealing with harmful in-store behaviour, but no additional rollouts had been confirmed. The company said each store would undergo privacy, legal and risk assessments before implementing the technology. Customers will also be notified through prominent signage when facial recognition is in use. Foodstuffs South Island said it would continue monitoring the system’s performance and update the list of participating stores on its website. To stay up to date on the latest industry headlines, sign up to the C&I e-newsletter.
Jun 9, 2026 · via c-store.com.au
Just days after a damning WIRED report exposed that Meta had quietly embedded facial recognition technology (FRT) code into millions of phones, the tech giant has quietly acquiesced in demands to reverse course. Last week, researchers identified code in Meta AI, a companion app for its line of smart glasses, that could convert images of faces into unique biometric signatures to identify strangers in public. EFF’s Threat Lab verified these findings through static analysis, and reminded consumers to think twice before buying or using Meta’s surveillance glasses. Just as quietly as Meta embedded this code, the app’s June 5th app update appears to have quietly removed all those features and systems. Gone is the face-recognition technology, the code meant to trigger “Person recognized” alerts, and the machine learning models and databases designed to detect, digitize, and store the biometric signatures of people users engage with. When WIRED broke the news last week, Meta’s executives immediately went on the defensive. Yet, their actions speak louder than their tweets: less than 48 hours after the public caught wind of their plans, Meta quietly launched an update to scrub nearly all traces of the FRT system from their app. But this quiet deletion of code does not equal a permanent change of heart. Meta previously used face recognition, and stopped only after it faced the legal and financial consequences. Now the company has refused to answer WIRED’s inquiries on whether it plans to bring the NameTag system back in the future, or what they did with any data they may have already collected during internal testing. There are billions of reasons not to turn Meta’s customers into a distributed surveillance machine. This whiplash behavior proves exactly why we cannot rely on the "good will" of Big Tech to protect our digital rights. We
Jun 9, 2026 · via eff.org
Meta quietly removes face-recognition code from its smart glasses app The 'disappearing into the bushes like Homer Simpson' strategy is a bold choice. Only a day after a dormant bit of code that seemed to be a facial recognition algorithm was discovered in a companion app for its smart glasses, Meta released an update which removed that code, Wired reported. The publication had first uncovered the suspicious code, internally dubbed Name Tag within Meta, while reviewing code for a Meta AI app which handles some core features of the glasses. In other words, the same app necessary for pairing Meta smart glasses to a user's phone over Bluetooth was also ready to start harvesting every face a user passed by while wearing them. Wired uncovered the dormant tool on June 4. It contained algorithms which would have converted photos of faces into biometric identifiers stored on-device and cross referenced with each new facial scan. On June 5, an update was released which removed it entirely. In February, The New York Times had reported that Meta was working to bring facial recognition to its glasses. Given that the Times heard the internal moniker Name Tag bandied about at that time, the code discovered by Wired was likely the fruit of those efforts. The workings of the tool suggest that it might have been intended as a way for users to more easily identify people they had previously met. A handy feature for forgetful folks, no doubt, but also an extremely creepy and invasive solution to a very common interpersonal dilemma. Most people would probably rather someone simply admit to having forgotten their name than to have their likeness ingested by a face-mounted camera. Meta smart glasses are made in partnership with popular Luxottica brands including Ray-Ban and Oakley. They are already
Jun 9, 2026 · via engadget.com
It seems that Meta is highly concerned about potential backlash to its use of facial ID in more applications. The company has removed its face ID functionality from the back-end code of its AI glasses after recent reporting on its inclusion. Last week, Wired reported that Meta had quietly added facial ID code elements into its Meta AI app. The elements were not active, but had been inserted in the codebase, seemingly with a view to future activation. Which is no surprise. In February, reports circulated that Meta was planning to add facial recognition to its artificial intelligence-powered sunglasses, as a means to enhance connection despite the privacy concerns. The inclusion of this in the Meta AI app aligns with that reporting. However, Meta responded angrily to Wired’s report, with Andy Stone, Meta’s spokesperson, saying in a June 4 post on X that Wired’s report was “shoddy,” “intellectually dishonest” and “pure advocacy-driven click bait.” Meta’s decision to criticize the reporting came before the company removed the code, seemingly as a means to invalidate the report. However, the evidence would suggest that Meta wants to add face ID to its AI device. It just wants to do so without sparking a massive PR backlash, which could potentially derail its evolving AI business. Meta’s hesitance here is logical, given its history with facial recognition elements. In 2021, Meta was forced to shut down its facial recognition processes on Facebook, after user backlash around the automated detection of faces in images, particularly via photo tagging. That sparked a large-scale investigation into the privacy concerns around face ID and the troves of data that Meta had been collecting, through this and other means. This, of course, also built on the Cambridge Analytica controversy, which had already led to various investigations into Meta’s data tracking.
Jun 8, 2026 · via socialmediatoday.com