Eleven 'far-right agitators' banned from UK ahead of rally, government says Eleven foreign "far-right agitators" have been blocked from entering the country to attend a rally organised by anti-Islam activist Tommy Robinson, the government has said. Thousands are expected to join the Unite the Kingdom event on Saturday, while an annual pro-Palestinian demonstration takes place in another part of London. Sir Keir Starmer said "we're in a fight for the soul of this country" ahead of the protest. The Metropolitan Police is anticipating one of its busiest days in recent years, and has drafted more than 4,000 officers over fears of clashes if the rival protests coincide. In a statement on Friday, Sir Keir said: "We're in a fight for the soul of this country, and the Unite the Kingdom march this weekend is a stark reminder of exactly what we are up against. "Its organisers are peddling hatred and division, plain and simple. We will block those coming into the UK who seek to incite hatred and violence. "For anyone who sets out to wreak havoc on our streets, to intimidate or threaten anyone, you can expect to face the full force of the law". The BBC has approached Unite the Kingdom for comment. In a post on X on Friday, Robinson - whose real name is Stephen Yaxley-Lennon - wrote: "Keir Starmer's regime are the enemy of the British people. Descend on London." Among the 11 blocked from entering the UK is the US-based, anti-Islam influencer Valentina Gomez, who attended the first Unite the Kingdom march last September. While that rally began largely peacefully, there were a series of violent confrontations with police and anti-Muslim hate speech incidents. Protesters are due to gather at Parliament Square on Saturday, while the separate pro-Palestine Nakba Day march begins at Waterloo
May 15, 2026 · via bbc.com
Abstract Facial recognition is increasingly adopted for automated classroom attendance; however, real-world deployment in schools remains constrained by privacy risks, ethical obligations, demographic bias, spoofing threats, and limited computational resources. Recent incidents involving Microsoft Teams in New South Wales in 2025 and Chelmer Valley High School in the United Kingdom show how poorly governed systems violate student rights and regulatory compliance. Despite growing adoption, many existing attendance systems focus narrowly on recognition accuracy or efficiency, while overlooking spoof resistance, open-set identity handling, fairness mitigation, auditability, and privacy protection. This paper presents LaED, a lightweight, edge-aware, and explainable deep learning framework for privacy-preserving classroom attendance in resource-constrained educational environments. The framework combines multimodal spoof detection, open-set facial recognition, and fairness-aware representation learning within a unified edge-based design. Spoofing attacks, including replay and deepfake attempts, are mitigated through the fusion of physiological and temporal facial cues, while unknown identities are explicitly rejected to reduce proxy attendance. To support responsible deployment, LaED incorporates federated learning with differential privacy, ensuring that biometric data remain local to schools while enabling accountable model updates. Experimental evaluation on CASIA-FASD, CelebA-Spoof, DFDC, FairFace, and a consent-driven classroom dataset shows that LaED achieves over 97.8% recognition accuracy, APCER and BPCER values below 2%, demographic fairness gaps under 2%, and inference latency below 150 milliseconds on edge hardware. Additional tests confirm reliable operation under realistic classroom conditions. These results demonstrate that regulation-aligned and trustworthy facial attendance is feasible on low-cost devices, offering a practical pathway for responsible biometric AI in education. Similar content being viewed by others Introduction Facial recognition technologies have rapidly gained traction for automating administrative tasks across diverse domains, including law enforcement, border control, healthcare, and education1,2,3. In classroom contexts, automated attendance systems are promoted as a means to reduce manual errors, save instructional time, and enable
May 15, 2026 · via nature.com
Inspired by the same core technology used in flagship smartphones, the Lock Vision Series projects over 20,000 infrared points to create highly accurate 3D facial maps capable of millimeter-level recognition. The system unlocks doors in under one second while effectively resisting spoofing attempts using photos or videos. Unlike traditional fingerprint-based solutions, the technology works reliably even when users are wearing glasses, hats, wigs, or makeup. Smarter, Hands-Free Unlocking for Every Household The Lock Vision Series offers multiple unlock methods, including 3D facial recognition, app control, NFC cards, passwords, voice assistants, smartwatch control, auto-unlock via geo-fencing, and physical keys. SwitchBot Lock Vision Pro further expands biometric access with contactless palm vein recognition and semiconductor fingerprint unlocking. Using near-infrared sensing technology, palm vein recognition enables users to unlock doors without touching the device, even when hands are wet, dirty, or difficult to scan with conventional fingerprint readers. By storing all biometric information locally with AES-128 encrypted communication, the Lock Vision Series prioritizes both privacy and security. Built for Long-Term Reliability To reduce concerns around battery anxiety and lockouts, the Lock Vision Series features SwitchBot's DualPower™ and DualBackup™ systems. A built-in 10,000mAh rechargeable battery can power the lock for up to 12 months on a single charge under typical usage conditions, while an additional CR123A backup battery provides up to 500 emergency unlocks. In emergency situations, users can also temporarily power the device via the USB-C emergency power port (the port provides temporary power and does not charge the lock). To further improve power efficiency, the lock utilizes mmWave radar detection, activating biometric recognition only when someone approaches the door. The SwitchBot app also provides real-time battery monitoring and low-battery notifications. Matter-over-WiFi Connectivity Without a Hub The Lock Vision Series supports Matter-over-WiFi, allowing users to connect directly with supported smart home ecosystems without
May 15, 2026 · via manilatimes.net
Today SwitchBot is debuting its all-new SwitchBot Lock Vision and Lock Vision Pro with some notable launch deals at up to $40 off via its official Amazon storefront. As detailed in our launch coverage, the highlights include Matter-over-WiFi with no SwitchBot hub required, the brand’s new 3D facial recognition, and, on the pro model, fingerprint recognition and contactless palm scanning tech that neatly slides into your Apple home setup. There are a pair of new models debuting today with launch discounts: - SwitchBot Lock Vision $130 (Reg. $170) - SwitchBot Lock Vision Pro $190 (Reg. $230) A complete rundown of everything you need to know on both models awaits right here, but we will leave you with some highlights from the new Matter-over-WiFi Lock Vision Series below: - Designed to fit most standard deadbolt locks for easy installation. - Matter-over-WiFi for Matter 1.4, and available to work on Apple Home (certificate in process). - Advanced Unlock Methods: Up to 19 unlock methods, including 3D structured-light facial recognition, contactless palm print recognition (Lock Vision Pro only), and semiconductor fingerprint unlocking (Lock Vision Pro only). - Advanced Power System: DualPower™ and DualBackup™ systems deliver up to 12 months of use on a single charge, with support for up to 500 emergency unlocks and Type-C emergency charging. - Advanced Security Protection: Includes alarm, locking, unlocking, communication, storage, and power security systems, along with IP65-rated water and dust resistance. FTC: We use income earning auto affiliate links. More. Comments
May 15, 2026 · via 9to5toys.com
SwitchBot launches world's first smart deadbolt lock with 3D facial recognition technology There are 9 other unlocking methods too QUICK SUMMARY SwitchBot has launched its new Lock Vision Series, introducing what it claims are the world’s first smart deadbolt locks with built-in 3D facial recognition technology. Available in standard and Pro models, the new locks combine fast facial unlocking with alternative methods including fingerprint scanning, palm vein recognition and app control, whilst also offering up to 12 months of battery life and simple DIY installation. Announced today, SwitchBot has launched its new SwitchBot Lock Vision Series, consisting of the SwitchBot Lock Vision and SwitchBot Lock Vision Pro. The new models are labelled as the world’s first smart deadbolt locks to feature advanced 3D structured-light facial recognition technology. Whilst we’ve seen facial recognition arrive on smart locks before – including last year’s SwitchBot Lock Ultra Vision Combo – SwitchBot says these are the first deadbolt-style smart locks to use the technology in this way. For now, the range is launching exclusively in the US, priced at $299.99 for the Lock Vision Pro and $169.99 for the standard Lock Vision. Both are available through Amazon and SwitchBot’s online store. The facial recognition system is inspired by the same type of technology used in flagship smartphones, projecting over 20,000 infrared points to create highly detailed 3D facial maps with millimetre-level accuracy. It’s also designed to work reliably even if you’re wearing glasses, hats or makeup – something cheaper facial recognition systems often struggle with. If facial recognition isn’t really your thing, both locks support a huge range of alternative unlocking methods too. Alongside app control and physical keys, users can unlock via NFC cards, passwords, voice assistants, smartwatches and auto-unlock geo-fencing features. The locks also support contactless palm vein recognition and semiconductor fingerprint
May 15, 2026 · via t3.com
TOKYO, May 15, 2026 /PRNewswire/ -- SwitchBot, a leading provider of smart home automation and embodied AI products, today announced the launch of the SwitchBot Lock Vision Series in North America, consisting of SwitchBot Lock Vision and SwitchBot Lock Vision Pro. Designed to elevate smart home entry, the Lock Vision Series is the world's first smart deadbolt lock equipped with advanced 3D structured-light faiacial recognition technology, delivering faster, safer, and more effortless unlocking for modern households. Inspired by the same core technology used in flagship smartphones, the Lock Vision Series projects over 20,000 infrared points to create highly accurate 3D facial maps capable of millimeter-level recognition. The system unlocks doors in under one second while effectively resisting spoofing attempts using photos or videos. Unlike traditional fingerprint-based solutions, the technology works reliably even when users are wearing glasses, hats, wigs, or makeup. Smarter, Hands-Free Unlocking for Every Household The Lock Vision Series offers multiple unlock methods, including 3D facial recognition, app control, NFC cards, passwords, voice assistants, smartwatch control, auto-unlock via geo-fencing, and physical keys. SwitchBot Lock Vision Pro further expands biometric access with contactless palm vein recognition and semiconductor fingerprint unlocking. Using near-infrared sensing technology, palm vein recognition enables users to unlock doors without touching the device, even when hands are wet, dirty, or difficult to scan with conventional fingerprint readers. By storing all biometric information locally with AES-128 encrypted communication, the Lock Vision Series prioritizes both privacy and security. Built for Long-Term Reliability To reduce concerns around battery anxiety and lockouts, the Lock Vision Series features SwitchBot's DualPower™ and DualBackup™ systems. A built-in 10,000mAh rechargeable battery can power the lock for up to 12 months on a single charge under typical usage conditions, while an additional CR123A backup battery provides up to 500 emergency unlocks. In emergency situations, users
May 15, 2026 · via prnewswire.com
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May 15, 2026 · via iopscience.iop.org
Facial Recognition Cameras to Debut in Peterborough City Centre Police introduce new technology to identify wanted people and boost public safety State-of-the-art facial recognition cameras will be used in Peterborough city centre for the first time this Saturday. Police are bringing in Live Facial Recognition (LFR) technology to help officers quickly spot individuals wanted for criminal offences or with outstanding arrest warrants. The system works by comparing faces in a live camera feed against a police watchlist, flagging possible matches for officers to review. Police say images flagged as a match are checked and then deleted after use, while other data is removed automatically. “LFR supports policing by identifying wanted people, ensuring they are brought to justice swiftly." Assistant Chief Constable John Massey said. “It is another tool at our disposal in our drive to reduce crime and create a safer county. “While this capability is new to Cambridgeshire, it has been used across policing and security services for some time and has improved greatly, even outperforming its anticipated accuracy and success rate. “We know the community will have a lot of questions about the use of advanced technology within policing and officers will be on hand to engage with the community and answer any queries they may have.” The equipment, borrowed from Bedfordshire Police, aims to make the city safer and tackle crime swiftly. Signs will mark the areas where the cameras are being used, with officers on hand to answer questions from the public.
May 15, 2026 · via planetradio.co.uk
You’re trying to book concert tickets before they sell out. You click the link and before you can make the payment, you’re asked to identify traffic lights, bicycles or blurry crosswalks in a grid of tiny images. Again. For many people, this has become a routine part of life. Logging into financial apps, shopping online or creating accounts increasingly involves “proving you are human”. These systems are known as CAPTCHA. Why are they everywhere? The short answer is that websites are fighting a rapidly escalating war against bots: automated software that imitate human behaviour online. And thanks to advances in artificial intelligence (AI), those bots are becoming even smarter, cheaper and harder to detect than ever before. Why websites need proof you are human Huge amounts of online traffic now come from automated systems. Some are helpful, such as search engine crawlers indexing pages for Google search. Others are far less welcome, and may involve phishing, spam, fake accounts, passwords violation, misinformation, and distributed denial of service attacks overloading web servers. In some areas, AI agents now generate automated online traffic that exceeds human traffic altogether. Modern AI systems can generate convincing text, imitate browsing patterns and even solve some CAPTCHA puzzles. At the same time, companies are increasingly worried about bots scraping online content to train AI systems. As a result, more websites are adding verification systems simply to keep abuse under control. How CAPTCHA actually works CAPTCHA stands for “Completely Automated Public Turing test to tell Computers and Humans Apart”. The original idea was simple: give users a task humans find easy, but computers find difficult. Early CAPTCHA systems often involved distorted text. Later versions switched to image-recognition tasks such as selecting all the squares containing traffic lights or bicycles. Google’s reCAPTCHA became one of the best-known examples.
May 15, 2026 · via theconversation.com
Facial recognition lawsuit raises questions about AI use in policing RENO, Nev. — Artificial intelligence is becoming more common in law enforcement, with agencies across the country using technology designed to help officers identify suspects, analyze information and respond faster. But a federal lawsuit filed in Nevada is raising new questions about what happens when that technology gets it wrong. The case, Killinger v. Jager/City of Reno, centers on an incident at the Peppermill Resort Spa Casino in Reno, where Jason Killinger was detained after facial recognition software allegedly misidentified him as a banned individual. According to court filings, police detained Killinger for identification after receiving the facial recognition alert. Fingerprints later confirmed he was not the person flagged by the system, and he was released. The lawsuit, filed in U.S. District Court in Nevada, alleges false arrest and unlawful detention under the Fourth Amendment. The complaint also claims officers relied too heavily on facial recognition technology without proper verification. RELATED: Baltimore County police release body cam of teen being detained, AI mistakes chips for gun RELATED: Increased use of artificial intelligence in police work could cause problems in cases The case has since been expanded to include the City of Reno as a defendant, raising broader questions about law enforcement policies, officer training, and safeguards surrounding the use of artificial intelligence. Experts interviewed by News 4 said the risks tied to facial recognition technology go beyond simple mistakes. “When these systems are wrong, the consequences are falling on real people,” said Drew Simshaw, a professor at the UNLV William S. Boyd School of Law. Simshaw said concerns include privacy, constitutional protections, and the possibility that some facial recognition systems may perform less accurately across different demographic groups, including people of color. “There could be Fourth Amendment concerns around tracking
May 15, 2026 · via mynews4.com
Some Syracuse Common Council members continue to advocate for a bill that would prevent some businesses from collecting biometric data.
Biometric data encompasses a whole host of information, including the shape of your face, eye color and even how you walk.
Recently, Onondaga County passed a law requiring businesses to notify customers when their biometric data is being collected.
Some members of the Syracuse Common Council are concerned about how that data will be used and its accuracy.
"We talk about embarrassment. We talk about increased interaction with law enforcement. There are a whole lot of negative consequences to being being mis-idenfitied,” said Corey Williams, a Democrat representing the city’s 3rd District.
The Common Council will vote on that bill on Monday.
The proposal comes after Wegmans recently deployed cameras equipped with facial recognition technology at select stores, saying some stores are using the technology to help with misconduct and retail theft, and that it doesn't collect other biometric data and disposes of the images and video after security purposes are fulfilled.
May 14, 2026 · via spectrumlocalnews.com
U.S. Army Sgt. 1st Class William Plummer, assigned to the U.S. Army Drill Sergeant Academy, receives a challenge coin during a Mental Health Champion recognition ceremony at Fort Jackson, South Carolina, Jan. 30, 2026. Plummer was recognized for supporting fellow Soldiers by identifying warning signs, reducing the stigma of mental health, and connecting teammates to appropriate resources. (U.S. Army photo by Sgt. 1st Class Dana Clarke)
| Date Taken: | 01.30.2026 |
| Date Posted: | 05.14.2026 14:05 |
| Photo ID: | 9683672 |
| VIRIN: | 260130-A-GV774-4053 |
| Resolution: | 4441x2965 |
| Size: | 1.32 MB |
| Location: | FORT JACKSON, SOUTH CAROLINA, US |
| Web Views: | 29 |
| Downloads: | 1 |
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May 14, 2026 · via dvidshub.net
On the morning of July 14, 2025, a team of U.S. Marshals arrived at Angela Lipps’s Tennessee home and arrested her at gunpoint while she was babysitting four young children. Lipps, a 50-year-old grandmother who had never flown on an airplane and had never set foot in North Dakota, found herself charged with eight felony counts of bank fraud in Fargo — a city she had never visited. The arrest warrant rested substantially on a facial recognition match generated by Clearview AI, a commercial system operating on a database of billions of photos scraped from the internet. No one from the Fargo Police Department called to question Angela before the warrant was issued. For 108 days, she sat in a Tennessee jail cell without bail, held as an accused fugitive awaiting extradition to face charges in a state she had never entered. When authorities finally reviewed her bank records in December, they revealed what any interview could have established months earlier: Lipps had been in Tennessee the entire time, her transactions placing her squarely at home while the actual fraud was occurring twelve hundred miles away. The charges were dismissed on Christmas Eve. The Fargo Police Department offered no apology and no explanation for why no investigator had spoken with her during five months of incarceration. The Fargo case is more than a cautionary tale about one flawed investigation. It exposes a growing institutional habit of treating AI outputs not as fallible signals requiring human verification, but as authoritative conclusions carrying near-prosecutorial weight. A Fargo detective reviewed Lipps’s social media and driver’s license, then concluded she matched the suspect based on “facial features, body type and hairstyle and color” — ratifying what the machine had decided rather than examining the evidence independently. The algorithm had become the accuser, and every
May 14, 2026 · via washingtonstand.com
From Patterns to Proof: The SEC’s New Playbook for Insider Trading Enforcement The SEC’s recent action charging 21 individuals in a wide‑ranging insider trading scheme marks an important inflection point in market abuse enforcement—both in scale and in method. While grounded in a familiar misappropriation theory, the case reflects a more fundamental shift: the Commission is now building scienter‑based antifraud cases through data analytics, pattern recognition, and network‑level investigation, rather than relying on isolated transactions or discrete tips. At its core, the case follows a recognizable model. A corporate attorney allegedly accessed confidential M&A information and transmitted it through a network of intermediaries and traders who executed transactions and, in many instances, shared profits through structured kickback arrangements. The conduct unfolded over multiple years, spanned more than a dozen transactions, and extended across a global web of participants. What distinguishes the case, however, is its construction. Rather than focusing on a single communication or trade, the SEC has assembled a cohesive narrative of repeated and coordinated conduct: the same actors trading ahead of multiple corporate events, recurring relationships among participants, and financial arrangements that reinforce deliberate collaboration. In this framework, the case is not about isolated misconduct—it is about a connected system of behavior revealed through data. From Transactional Cases to Network Enforcement This action exemplifies a broader evolution in SEC enforcement. Commission staff, including experts focus almost exclusively on insider trading, are increasingly able to identify anomalous trading activity across issuers, accounts, and time horizons, leveraging sophisticated data analytics and surveillance capabilities. Patterns that may appear fragmented at the participant level—trading in different securities, across accounts, or through intermediaries—can now be reconstructed into a coherent narrative through a combination of analytical techniques. Event‑driven analysis allows regulators to identify trading activity consistently occurring ahead of market‑moving developments, while network mapping
May 14, 2026 · via freshfields.com
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May 14, 2026 · via youtube.com
Police pounce on man and woman strolling through Piccadilly Gardens after checking their camera It's the latest time the technology has been used in the city Two people have been arrested and a missing man found after live facial recognition cameras were used in Manchester. Greater Manchester Police deployed the technology in the city centre on Wednesday (May 13). Cameras were used in the Piccadilly Gardens area. During the deployment, a 19-year-old man wanted on suspicion of shoplifting offences in Salford was picked up by the cameras. A woman wanted for offences including harassment and breach of bail was also spotted by the technology. Both people were arrested by officers at Piccadilly Gardens. Click here to get the biggest stories straight to your inbox in our Daily Newsletter GMP says a man who had previously been reported missing was also found safe and well using the technology. He has since been 'safeguarded' by police, the force says. "The two LFR vans were in city centre to support our neighbourhood policing operation and keep our communities safe," a GMP spokesperson added. Live facial recognition was introduced by GMP last autumn. Specialist police vans are fitted with fixed cameras, with computers inside using biometric data to match faces in the crowd to those on a 'watchlist'. Signs warn members of the public if they are entering the cameras' 'zone of recognition', and GMP says the cameras are only used to find people police are already looking for. Chief Inspector Mike Tachauer told the Manchester Evening News last year that Piccadilly Gardens is a key location to use the technology. "We speak to our community - our businesses, commuters and other users of the city centre," he said. "We know that Piccadilly Gardens is a priority for those people. We know that there
May 14, 2026 · via manchestereveningnews.co.uk
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May 14, 2026 · via iopscience.iop.org
FORT BLISS, Texas — Sergeant Major of the Army Michael Weimer conducts a Leader Professional Development (LPD) session with Class 76 students of the Sergeants Major Course at the U.S. Army NCO Academy, May 11, 2026. During his visit, SMA Weimer toured multiple classrooms, posed for group photos with students, and presented a coin of excellence to a staff member in recognition of their outstanding performance.
| Date Taken: | 05.11.2026 |
| Date Posted: | 05.13.2026 18:33 |
| Photo ID: | 9682376 |
| VIRIN: | 260511-A-QP427-1891 |
| Resolution: | 4225x2476 |
| Size: | 1.07 MB |
| Location: | FORT BLISS, TEXAS, US |
| Web Views: | 11 |
| Downloads: | 0 |
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May 14, 2026 · via dvidshub.net
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May 14, 2026 · via youtube.com
FORT BLISS, Texas — Sergeant Major of the Army Michael Weimer conducts a Leader Professional Development (LPD) session with Class 76 students of the Sergeants Major Course at the U.S. Army NCO Academy, May 11, 2026. During his visit, SMA Weimer toured multiple classrooms, posed for group photos with students, and presented a coin of excellence to a staff member in recognition of their outstanding performance.
| Date Taken: | 05.11.2026 |
| Date Posted: | 05.13.2026 18:33 |
| Photo ID: | 9682380 |
| VIRIN: | 260511-A-QP427-1991 |
| Resolution: | 3122x2705 |
| Size: | 872.58 KB |
| Location: | FORT BLISS, TEXAS, US |
| Web Views: | 14 |
| Downloads: | 1 |
This work, SMA Weimer Engages Future Leaders at the U.S. Army NCO Academy [Image 23 of 23], by SGM Valerie Sturdivant, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
May 14, 2026 · via dvidshub.net