Growing Demand for Real-Time Processing, Privacy, and Intelligent Devices Fuels Market Expansion SAN FRANCISCO, June 17, 2026 /PRNewswire/ -- The global on-device AI market is entering a new era of accelerated growth as enterprises and consumers increasingly prioritize real-time intelligence, privacy-focused computing, and low-latency digital experiences. According to recent industry analysis by Grand View Research, the global on-device AI market was valued at USD 10.76 billion in 2025 and is projected to reach USD 75.51 billion by 2033, registering a CAGR of 27.8% from 2026 to 2033. The market is witnessing substantial momentum as artificial intelligence capabilities move beyond centralized cloud infrastructure and become embedded directly into devices. From smartphones and wearables to connected vehicles and industrial equipment, organizations are leveraging on-device AI to improve responsiveness, reduce network dependency, and enhance data security. Shift Toward Edge Intelligence Accelerates AI Adoption Artificial intelligence deployment strategies are rapidly evolving. Businesses are increasingly recognizing the benefits of processing data directly on devices rather than relying solely on cloud-based systems. On-device AI enables applications to perform inference locally, allowing devices to respond instantly to user interactions while minimizing latency. This capability has become particularly valuable for applications requiring real-time decision-making, including voice assistants, image recognition, predictive maintenance, navigation systems, and smart automation. The continued expansion of connected devices and edge computing ecosystems is creating strong demand for localized AI capabilities. As enterprises seek faster and more reliable digital operations, on-device AI is becoming a critical component of next-generation technology infrastructure. Privacy and Data Security Become Strategic Priorities One of the most significant factors supporting market growth is the increasing emphasis on privacy and data protection. Organizations across industries are facing mounting regulatory requirements and consumer expectations regarding how personal information is collected, processed, and stored. By keeping sensitive data on the device rather than
Jun 17, 2026 · via prnewswire.co.uk
Figures Abstract Accurate classification of terrestrial habitats is critical for biodiversity conservation, ecological monitoring, and land use planning. Several habitat classification schemes are in use, typically based on analysis of satellite imagery and validation by field ecologists. Here, a methodology is presented for classification of habitats based solely on ground-level imagery (photographs), offering improved validation and enhanced ability to classify habitats at scale (e.g., using imagery from citizen science). In collaboration with Natural England, a public sector organisation with responsibility for nature/biodiversity conservation in England, this study develops a classification system that applies deep learning to ground-level habitat photographs, categorising each image into one of 16 distinct classes following the established ‘Living England’ framework. Images were pre-processed using resizing, normalisation, and augmentation techniques, while resampling was used to balance classes in the training data and enhance model robustness. A custom deep learning classifier based on the DeepLabV3-ResNet101 architecture was developed and fine-tuned to assign a habitat class label to ground-level photographs. Using five-fold cross-validation, the model demonstrated strong overall performance across 16 habitat classes, with accuracy and F1-scores varying between classes. This approach supports robust, scalable habitat classification based on balanced and well-prepared training data. Across all folds, the model achieved a mean F1-score of 0.63, with some habitat classes such as Bare Sand (BS) and Coniferous Woodland (CW) reaching values above 0.87. High performance was achieved for visually distinct habitats and lower performance for visually mixed or ambiguous classes. These findings demonstrate the potential of this approach for ecological monitoring. Ground-level imagery is easily obtained and accurate computational methods for habitat classification based on such data have many potential applications. To support use by practitioners, a simple web application is also provided that allows classification of uploaded images using the trained model. Citation: Tourian M, Vandaele R, Rowlands S,
Jun 17, 2026 · via journals.plos.org
Redditors Spent Years Trying to Find Mysterious Photo Known as ‘Celebrity Number Six’ An innocent Reddit post asking for help identifying the face of a mystery celebrity would spark a four-year-long international search involving facial recognition technology and photo copyright infringement. As Boing Boing reported yesterday, the wild story — that became known as “Celebrity Number Six” — began in 2020 when a Finnish Reddit user posted a photo of curtains that had eight famous faces on them. Reddit quickly identified all the celebrities, including Orlando Bloom, Jessica Alba, and Josh Holloway. All except one face. The Search for Celebrity Number Six As the source photo for each famous face was tracked down, number six continued to evade the Reddit sleuths, who couldn’t even agree on the gender of the person. The original post was shared to r/TipOfMyTongue, but the search for number six widened to other subreddits and, eventually, r/CelebrityNumberSix was created to focus efforts. According to Wikipedia, one Redditor searched through every picture taken by a photographer-of-interest between 1998 and 2007 on the Getty Images archives in a bid to find number six. But no luck. The community began keeping spreadsheets of all the possible celebrities it could be. Popular guesses included Olivia Wilde or Taylor Kitsch, but no source photo could be found. Leticia Sardá A Reddit user took the image from the curtains, colorized it, and then ran it through facial recognition website PimEyes. The tool returned a prominent name: Leticia Sardá. A Spanish Reddit user and member of r/CelebrityNumberSix contacted photographer Leandre Escorsell, a fellow Spaniard who photographed Sardá. Escorsell was shown the image and recognized it immediately. Escorsell expressed disappointment that his photo had been used on the fabric without permission, but he sent over the original photo to put the mystery to bed
Jun 17, 2026 · via petapixel.com
Back to Journals » Clinical Interventions in Aging » Volume 21 Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives Authors Zhang Z, He Y , Mo Z, Zhang P, Tian Z, Huang L Received 5 March 2026 Accepted for publication 4 June 2026 Published 18 June 2026 Volume 2026:21 607232 DOI https://doi.org/10.2147/CIA.S607232 Checked for plagiarism Yes Review by Single anonymous peer review Peer reviewer comments 3 Editor who approved publication: Dr Maddalena Illario Zhaochen Zhang,1,* Yuxi He,2,* Zhanhao Mo,3,* Peng Zhang,4 Zhenya Tian,5 Lanfeng Huang1 1Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin, People’s Republic of China; 2Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, Jilin, People’s Republic of China; 3Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, Jilin, People’s Republic of China; 4Department of Radiology, The Second Hospital of Jilin University, Changchun, Jilin, People’s Republic of China; 5The First Norman Bethune Clinical Medical College, Jilin University, Changchun, Jilin, People’s Republic of China *These authors contributed equally to this work Correspondence: Lanfeng Huang, Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin, People’s Republic of China, Email [email protected] Abstract: Osteoporosis (OP) is a chronic systemic skeletal disorder that predominantly affects the elderly. It is characterized by an imbalance in bone homeostasis, reduced bone mass, microarchitectural deterioration of bone tissue, and increased bone fragility, ultimately leading to a higher risk of fractures and related complications. With the progression of global population aging, the prevalence of OP continues to rise, underscoring the importance of early diagnosis and timely intervention. However, the diagnosis and management of OP—particularly its early detection—remain limited by material constraints such as diagnostic equipment and by subjective factors including clinician experience, which hinder widespread screening. In recent years, artificial intelligence (AI)
Jun 17, 2026 · via dovepress.com
Local TV station operator Sinclair is going to let viewers interact with its on-air programming — and ads — in a new way under a partnership with IRCODE. Sinclair has made a strategic investment in IRCODE, a computer vision and AI company that claims its system makes TV interactive and shoppable in real time. The amount of the investment isn’t being disclosed. Other IRCODE investors include Craig Kallman, former chairman and CEO of Atlantic Records who recently was named chief music officer of Warner Music Group. Alongside the investment, Sinclair will roll out IRCODE-powered interactive television at its stations in Salt Lake City and Austin beginning in July 2026, with additional markets to follow through the year. The deployments will let the stations make interactive television a native capability inside its own platforms. Popular on Variety According to IRCODE, its real-time image recognition system works like “Shazam for images.” It identifies what is on screen (without QR codes) and connects the viewer to relevant content and commerce. In Sinclair’s implementation, viewers can open the app for the local Sinclair station and point their phone at the TV. At that point, the app kicks off an action to let the viewer purchase a product, enter to win a sweepstakes or link to additional info. Because it happens inside the station’s app, the viewer is identified and opted in, and every step from first scan to conversion is captured as first-party data. For example, advertisers running spots on Sinclair’s stations in Salt Lake City or Austin can now measure who engaged, what they did next, and whether it led to a purchase. Using their smartphone cameras and the Sinclair app, viewers can participate in real-time polls, comment on local news and interact with advertisers, said Del Parks, president of technology. “IRCODE is
Jun 17, 2026 · via variety.com
SNS has published a guide addressing growing demand for AI-powered video indexing, transcription, facial recognition, and searchable metadata across media libraries, positioning its AI Suite platform as an on-premise solution to these requirements. The SNS AI Suite performs automatic speech-to-text transcription with multi-language translation, multi-speaker diarization, facial recognition and identity mapping, custom AI model training for specific faces and voices, object and scene detection, OCR text extraction from on-screen graphics, and natural language search across indexed metadata. All processing runs on-premise under a flat perpetual license with unlimited processing hours, without cloud processing fees or external data transmission. The guide contrasts on-premise AI processing with cloud-hosted alternatives, citing two primary concerns with cloud approaches for organizations processing large video volumes: unpredictable cost scaling as processing volume grows, and data exposure when proprietary footage or biometric information is transmitted to external platforms. Key evaluation criteria the guide recommends for organizations selecting an AI media indexing system include custom training capability, scalability, licensing model predictability, and on-premise versus cloud architecture. All AI-extracted metadata — including transcripts, speaker logs, face mappings, object tags, and scene descriptions — is indexed into a searchable on-premise database with timecode-level precision. The AI Suite includes an API for integration with external MAM and DAM systems at no additional charge and can be deployed alongside EVO or existing storage infrastructure. More information and demo access is available at snsevo.com.
Jun 17, 2026 · via sportsvideo.org
25 Years of Inspection Technology: What Has Changed, What Hasn’t, and What Still Matters Twenty-five years of transition from paper clipboards to AI image recognition have drastically accelerated inspection speed, yet checkbox fatigue and unresolved hazards remain. - By Naaman Shibi - Jun 17, 2026 Cast your mind back to 1999. Most workplace inspections were done on paper. Clipboards, carbon copies, filing cabinets. A completed checklist was folded, handed to a supervisor and often never looked at again unless something went wrong. Deficiencies lived in a drawer. Follow-up was whoever remembered to follow up. A lot has changed since then. And some things, frustratingly, haven’t changed at all. What Has Changed The Medium The most visible shift has been the move from paper to digital. Over the past 25 years, clipboards gave way to PDAs, PDAs gave way to tablets and smartphones, and smartphones gave way to purpose-built mobile applications designed specifically for field data collection. Today, an inspector on a mine site, a construction scaffold or a warehouse floor can complete a structured inspection, photograph a deficiency, assign a corrective action and submit a report before they have walked back to the site office. That workflow used to take days. The Data Paper inspections produced records. Digital inspections produce data. That distinction matters more than it might first appear. A paper record tells you what happened at a point in time. Digital data, accumulated across hundreds or thousands of inspections, tells you what is happening across your entire operation. Trends become visible. Patterns emerge. You can see that the same piece of equipment fails its pre-start check every third Monday, or that one particular site consistently generates corrective actions that are never closed. That kind of insight was impossible to extract from a filing cabinet. Speed And Accountability The
Jun 17, 2026 · via ohsonline.com
U.S. Army Spc. Peter Wenzel, with bravo company, 628th Aviation Support Battalion, 28th Expeditionary Combat Aviation Brigade, is awarded the Army Commendation Medal and a commemorative plaque in recognition of winning both the brigade and state Best Warrior Competitions. (U.S. Army National Guard photo by Command Sgt. Maj. Jeff Huttle)
| Date Taken: | 06.12.2026 |
| Date Posted: | 06.17.2026 11:22 |
| Photo ID: | 9745396 |
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| Resolution: | 4032x3024 |
| Size: | 3.89 MB |
| Location: | FORT INDIANTOWN GAP, PENNSYLVANIA, US |
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Jun 17, 2026 · via dvidshub.net
Evan Spiegel doesn't want you to call Snap Specs AI glasses Snap's CEO sat down with Engadget after his keynote at AWE. Snap's newly announced AR Specs might seem similar to other smartglasses, but Snap CEO Evan Spiegel says that's the wrong way to think about the product. Specs, he says, is "a new type of computer, a see-through computer." Shortly after unveiling Specs at AWE, Spiegel sat down with Engadget to tell us more about the device we got a glimpse of onstage. The CEO repeatedly referred to Specs as a "computer" and that really is core to understanding how Snap is positioning the product (and justifying the price). Specs, Spiegel said, "is able to overlay computing on the world around you and bring computing into the world, which is so important if you want to make computing feel more human." But Snap will have to do more than just persuade people to buy a computer for their face. When Specs go on sale later this year, the company will face a very different environment than when it first started experimenting with camera-enabled glasses in 2016. For one, it has a lot more competition now. But today, there's also increasing suspicion of smartglasses, given that there have been some very public cases of people misusing the tech. There's the Meta of it all, too. The company was recently caught with an unreleased facial recognition feature on its Ray-Ban glasses (that it removed soon after outside researchers discovered it). Spiegel, not surprisingly, isn't a fan of facial recognition. "There are certain use cases, like facial recognition, that we don't allow in Lenses, and one of the benefits of having our own developer ecosystem and our own developer tools is that we're able to moderate the Lenses that are submitted and
Jun 16, 2026 · via engadget.com
Meta Ray-Bans have been under increased public scrutiny following revelations about the facial recognition work Meta has been doing on its smart glasses. Consumers are rightly wary of products that could convert wearable tech into everyday surveillance devices. In early June, an investigation by Wired exposed how Meta had quietly embedded code for dormant facial recognition software under the internal designation "NameTag." The feature, if rolled out, could have allowed Meta smart glasses to biometrically identify anyone in view -- in real time, without consent -- using a stored digital faceprint. The code, which was never made active for users, was removed a day later. The Electronic Frontier Foundation's Threat Lab verified the initial findings and reported that Meta reversed course following public blowback. But the privacy nonprofit noted that Meta deleting the code "does not equal a permanent change of heart." Now, just a week after Meta removed the code, the company is facing new questions about its facial recognition software prototype. A new investigation by Wired uncovered that Meta partnered with Rank One Computing, a supplier for the US military and law enforcement agencies, for its biometric identification technology. Wired said it uncovered a software license tying the Pentagon vendor to the Meta AI app, the same one used for Meta's smart glasses products. The license agreement would authorize Meta to use Rank One's military-grade facial recognition and "liveness detection," which confirms whether someone is seeing a live person or a mask or photo. This business relationship, as Wired pointed out, "shows how thin the line has grown between the surveillance technology sold to law enforcement and the military and the consumer products sold to everyone else." According to Wired, Rank One Computing declined to comment on the findings. The Denver-based firm, which earns roughly 80% of its
Jun 16, 2026 · via cnet.com
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Jun 16, 2026 · via tampabay.com
Most AI demos today can talk really well, but they can't actually do anything useful. In this project, we're going to change that by building a voice-controlled AI assistant using the open-source XiaoZhi AI project and the feature-packed DFRobot ESP32-S3 AI Camera. What results isn't just a chatbot—it's a real, functional AI companion that can see its surroundings, control hardware, and even manage your calendar. This assistant lets you turn lights on and off, fetch sensor data, take photos on command, and conduct real visual recognition conversations. The best part? The entire project is open source under the MIT license, meaning you can use it for free, even in commercial applications. If you're ready to build an AI that's actually useful, read on. Get PCBs for Your Projects ManufacturedYou must check out PCBWAY for ordering PCBs online for cheap! You get 10 good-quality PCBs manufactured and shipped to your doorstep for cheap. You will also get a discount on shipping on your first order. Upload your Gerber files onto PCBWAY to get them manufactured with good quality and quick turnaround time. PCBWay now could provide a complete product solution, from design to enclosure production. Check out their online Gerber viewer function. With reward points, you can get free stuff from their gift shop. Also, check out this useful blog on PCBWay Plugin for KiCad from here. Using this plugin, you can directly order PCBs in just one click after completing your design in KiCad. What You'll NeedHardware- 1 × DFRobot ESP32-S3 AI Camera – This is the star of the show. It comes with an onboard microphone, speaker, camera, and ample processing power. - 1 × Type-C USB cable – For power and programming. - XiaoZhi AI Firmware – The core open-source ESP32 chatbot project. - ESP-IDF or pre-built firmware
Jun 16, 2026 · via hackster.io
Smart Glasses: Meta allegedly tested facial recognition from Pentagon supplier Meta tested facial recognition for smart glasses using tech from a US provider close to authorities. Traces found in Meta AI app. Meta is said to have relied on technology from a US company that primarily supplies the military, intelligence agencies, and law enforcement agencies for the development of a possible facial recognition feature for its smart glasses. This is according to a software license from the company Rank One Computing (ROC) that Techmagazin Wired has obtained and is said to be linked to a test version of the Meta AI app. The Meta AI app is required for setting up and for central functions of the Ray-Ban Meta glasses and other smart glasses from the company. Wired already reported in early June about inactive program code for a facial recognition function developed by Meta in the Meta AI app. Shortly after the report was published, Meta largely removed the code, internally called “Nametag”, via an update. Videos by heise Traces of external facial recognition systems According to the new Wired report, the software license acquired by Meta includes not only ROC's facial recognition but also a function that checks whether a camera is capturing a live person, a photo, or a mask. The license is said to support up to ten million facial templates. Wired found traces of the software in a version of the Meta AI app that was reportedly distributed to users in June. This included components for checking the license and starting the software. However, these functions were not activated, just like Meta's own facial recognition software. Rank One Computing is a Denver-based company that develops facial recognition technology and generates most of its revenue from government clients. It was founded in 2015 by engineers who
Jun 16, 2026 · via heise.de
- Researchers at the University of Cambridge recently developed the world’s first vaccine designed by artificial intelligence (AI) and successfully tested it in humans. - The vaccine was created to protect against viruses in the sarbecovirus family, including both SARS and SARS-CoV-2, the virus that causes COVID. - Medical News Today spoke with a virologist and an AI scientist to discuss vaccine safety and effectiveness, and how AI can help scientists develop universal vaccines. The world’s first human vaccine designed by artificial intelligence (AI) and developed by scientists at the University of Cambridge has passed its initial testing successfully and is currently undergoing further testing. What makes this vaccine different from traditional vaccines is that, instead of being developed in response to current strains, it uses a predictive design. To achieve that, scientists used AI to analyze multiple coronaviruses to create a “super antigen“. With this super antigen, they were able to target the common features of viruses in the coronavirus family and, in a sense, future-proof the vaccine against current and future coronavirus mutations. The vaccine also has a needle-free design, using a specialized jet injector via the PharmaJet Tropis system. Rather than piercing the skin with a traditional metal syringe, it uses fluid dynamics to deliver the vaccine ingredients exactly where they need to go. However, the technology remains highly experimental, and the first human trial included only 39 people. The results of the trial were published in the Journal of Infection. To decipher what this trial means for the future of vaccine development and how this technology works, Medical News Today spoke to two experts who were not involved in the research: - Monica Gandhi, MD, MPH, an infectious disease specialist and professor of medicine at the University of California, San Francisco, - and Marc Boubnovski, senior
Jun 16, 2026 · via medicalnewstoday.com
In January 2020, a Finnish Reddit user posted a photo of curtains printed with eight celebrity faces and asked for help identifying them. Seven were quickly matched — Josh Holloway, Jessica Alba, Orlando Bloom, and others prominent in the mid-2000s. The sixth face, dubbed Celebrity Number Six, resisted identification for four years. A dedicated subreddit pursued hundreds of leads. Members contacted the fabric supplier (Látky Mráz in the Czech Republic, who wouldn't name the designer), reviewed a decade of Getty Images photos, compiled spreadsheets, and tried every form of reverse image searching. Even the gender of Number Six was disputed. One user reviewed "all photographs on Getty Images between 1998 and 2007" that shared a photographer with any of the known matches. In 2024, a Redditor colored in the image and ran it through the facial recognition tool PimEyes, which returned Spanish model Leticia Sardá as a likely match. Another user contacted the photographer, Leandre Escorsell, who confirmed he'd taken the original photo in 2006 for a Spanish magazine — an image that had "never appeared on the Internet." He was surprised anyone had found it. Sardá, who had quit modeling in 2009 and was working as a waitress, told the New York Times, "he tried to explain, but I didn't realize it was going to be so big." She has since resumed modeling.
Jun 16, 2026 · via boingboing.net
Meta uses your public Facebook posts for AI search Meta is now using public posts from across its social platforms to power AI-generated search results on Facebook. The search option, AI Mode, appears alongside standard search filters and generates responses drawn from publicly shared content, according to The Verge. The move is part of a broader rollout of Meta's Muse Spark model, which launched in April — the fruit of an internal AI reorganization that the company's CTO recently called "atrocious." You May Also Like Muse Spark, the first AI model to come out of Meta Superintelligence Labs, was designed with everyday personal use in mind — health, shopping, visual understanding, and social content. Meta says the model will eventually pull its recommendations from across the company's empire of social apps: Instagram, Facebook, and Threads. The approach mirrors what Google has done with Reddit threads in its AI Overviews. The main difference is that Meta is leaning on its own ecosystem of user-generated content. Users can also ask Muse Spark follow-up questions based on the results it generates. The privacy implications, however, are worth watching. Meta says the feature relies on content people have already chosen to share publicly. Unmentioned was the fact that those users may not know a "public post" is being used to train a commercial AI product. Muse Spark is available now at meta.ai and in the Meta AI app. The company says a more advanced reasoning mode called Contemplating is in the works, but it has no confirmed release date. Topics Artificial Intelligence Meta Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat
Jun 16, 2026 · via mashable.com
BuzzFeed GamesCan You Crack Today’s Color Puzzle?Huedoku #45! It’s Tuesday — warm up those color receptors. 🎨🔥Posted 20 hours agocommentFacebookPinterestLinkby Crystal RoBuzzFeedBuzzFeed StaffHi, I’m Crystal, a Senior Editor based in Los Angeles and creator of BuzzFeed’s “That Got Dark” newsletter. Huedoku is a daily color puzzle with simple rules and a satisfying solve. It's like sudoku, but with colors instead of numbers. Sign up here to get notified every time we publish a new Huedoku! Come back tomorrow for Huedoku #46 — and share your score to challenge a friend! 🌟 🌈 New Huedoku drops every weekday at 4:00 a.m. PT / 7:00 a.m. ET. 🧩🗂️ Huedoku Archive — Every past puzzle, all in one place! Comments
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Jun 16, 2026 · via buzzfeed.com
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Jun 16, 2026 · via youtube.com
Snap defies investor pressure with ‘Specs’ smart glasses launch
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Jun 16, 2026 · via ft.com
greenbutterfly - stock.adobe.com Scottish minister clarifies police facial-recognition approach The Scottish government has confirmed its intention to ensure police use of facial recognition is lawful before deployments start taking place, unlike in England and Wales where the technology has been rolled out in a ‘legal vacuum’ without any formal scrutiny or debate Scotland will ensure that any police use of facial recognition technology is “lawful, effective, proportionate and grounded in respect for human rights,” according to the Scottish justice secretary. Writing to Scottish biometrics commissioner Brian Plastow – who urged the Scottish government in late May 2026 to introduce primary legislation for the police use of live facial-recognition (LFR) technology – Neil Gray, the cabinet secretary for justice, said the Scottish government will actively be monitoring how Westminster approaches the development of a new legal framework for the use of biometrics in law enforcement. Gray also said that while Scottish policing bodies are at least two years away from having a workable business case for the technology, he has taken note of Plastow’s “view that the introduction of primary legislation to Parliament would represent the optimal approach to establishing a statutory basis and enabling framework for the limited and proportionate use of LFR by Police Scotland”. Gray added although it would be “premature” for the Scottish government to commit to primary legislation on LFR given these circumstances, it stands ready to work with Plastow and others to ensure that any use of the technology is “lawful, effective, proportionate and grounded in respect for human rights”. He further added that any draft legislation for police biometrics published in England and Wales will be “carefully assessed” by the Scottish government for both its applicability and implications. The clarification on how Scotland will approach police LFR follows the Kings Speech in mid-May 2026,
Jun 16, 2026 · via computerweekly.com