Aldi rival rolls out new face-scanning policy to compare against 'watchlist'
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Image Recognition
Trust Stamp, Seiko Solutions partner on <b>facial recognition</b> service for Japan
Trust Stamp, Seiko Solutions partner on facial recognition service for Japan Seiko Solutions and U.S. digital identity firm TrustStamp are launching a joint project to bring a new facial recognition service to Japan, aiming for a full rollout in fiscal 2026. The companies say they will study system integration and advance real‑world deployment across multiple sectors. The partnership focuses on facial recognition used for admission control at entertainment venues, fraud prevention in financial and payment services, and identity verification for online platforms. Seiko Solutions, based in Chiba, will incorporate TrustStamp’s technology into its authentication center business and help build operational systems for companies adopting the service. The authentication center will act as a shared platform offering facial‑recognition‑based identity verification. Privacy protection is touted as a key feature. Matching will be performed without retaining image data, allowing the companies to offer biometric authentication while limiting the storage of personal information. The technology has already been tested at a live event, where pre‑registered facial data was successfully matched with images captured from attendees during check‑in. The trial confirmed that facial recognition and entry management could operate together smoothly, and development is continuing ahead of the planned nationwide launch. Demand for digital identity verification has grown in Japan as remote and online transactions expand, particularly in financial services governed by strict identity verification rules under the Act on Prevention of Transfer of Criminal Proceeds. Article Topics biometric matching | biometrics | facial recognition | identity verification | Japan | Trust Stamp Comments
Police begin live <b>facial recognition</b> trials in Chester city centre | Local News
Police begin live facial recognition trials in Chester city centre By Dherran Titherington 15th Jul 2026 The first of four planned live facial recognition (LFR) operations has taken place in Chester city centre. Operation Vigilant was carried out on Saturday 11 July with support from neighbourhood officers, the Sexual Offender Management Unit (SOMU), Project Servator officers and Greater Manchester Police's LFR team. Police said the busy city centre provided an opportunity to use the technology to identify people wanted for serious offences and safeguard vulnerable individuals. Officers also spoke with members of the public about how the system works, the privacy safeguards in place and how images are processed. Visitors were invited to watch the technology in use and ask questions. During the operation, officers issued three Community Resolutions for Class A drug possession and seized an illegally used e-bike. Police also said there were no reports of anti-social behaviour within the deployment area. A vulnerable woman was also reunited safely with friends after officers became concerned for her welfare. No arrest was made. A second deployment is planned later this month. Cheshire Police says it is expected to be the first in the UK to use authorised watchlist images from neighbouring police forces, allowing officers to identify wanted people across force boundaries. Detective Chief Inspector Robert Pritchard said: "Our aim under Operation Vigilant is to protect vulnerable people, safeguard those at risk and bring offenders to justice. "The use of Live Facial Recognition technology strengthens our ability to achieve those objectives and helps us prevent harm before it occurs. "This deployment forms part of our ongoing commitment to tackling violence against women and girls and ensuring our communities remain safe places to live, work and visit. "The positive outcomes from this operation demonstrate the value of combining innovative technology
Draconian change coming to major California grocery store chain — and shoppers are furious
Draconian change coming to major California grocery store chain — and shoppers are furious See more of our coverage in your search results. Add The California Post on GoogleA Bay Area grocery chain is bringing in facial recognition technology to try to snare shoplifters — but regular customers are furious. The move is set to be deployed at some Grocery Outlet stores, concerning shoppers their privacy could be infringed upon. The Emeryville-based chain is using software called SAFR, and shoppers will be notified that facial recognition is being used to catch people suspected of shoplifting. One store in Pleasant Hill, in Contra Costa County, will use the technology. California has one of the worst records in the US on shoplifting, as it has skyrocketed 50% since the pandemic. Experienced store manager June Guerrero says she understands why the brand is using facial recognition. “I worked for years as a manager of a store and the theft was just unbelievable,” Guerrero told CBS News. “I agree with it.” Shoppers think privacy and accuracy are their top concerns. “I do understand, but invading my privacy with my picture. I don’t agree on that,” Barbara Jackson told CBS News. “You gotta find a better way.” “It could lead to a lot of problems, I think for companies and businesses and people,” Steve Burdette added. An attorney who is an expert in digital privacy said the technology presumes guilt by scanning every person who enters the store. “This is a dragnet that scans everyone. Even if you’ve done nothing wrong, your face is being scanned,” Mario Trujillo, who works with the Electronic Frontier Foundation, said. “What you’re essentially doing is violating the privacy rights of every customer who walks into your store.” Sign up for the California Morning Report newsletter California's top news, sports
Kevin Feige to Receive Pioneer of the Year Award
Ahead of the release of Avengers: Doomsday, Marvel head Kevin Feige is being honored as Pioneer of the Year at a special event this fall. The Will Rogers Motion Picture Pioneers Foundation bestows the Pioneer of the Year Award upon esteemed members in the motion picture industry in recognition of leadership, service to the community and commitment to philanthropy. The annual celebration will be held Sept. 30 at the Beverly Hilton, and all proceeds raised at the event benefit WRMPPF’s Pioneers Assistance Fund, which provides assistance to working and retired individuals in the motion picture distribution and exhibition community in times of need. Related Stories “Kevin Feige is a dynamic producer and executive whose creativity and vision entertains and inspires moviegoers everywhere,” Kyle Davies, president of distribution at Bleecker Street Media and co-chairman, Pioneers Assistance Fund Committee, said in a statement. “The Will Rogers Pioneers Assistance Fund is proud to celebrate Kevin’s monumental cinematic achievements and extraordinary leadership by presenting him with the 2026 Pioneer of the Year Award.” Feige is one of Hollywood’s most successful producers, having been behind 37 feature films in the Marvel Cinematic Universe that have collectively grossed more than $32 billion worldwide, with 11 films surpassing $1 billion globally. This year he has both Avengers: Doomsday, set for a December release, and Spider-Man: Brand New Day, coming at the end of this month. He joins previous Pioneer of the Year honorees including Kate Hudson, Greta Gerwig, Erik Lomis, Barbara Broccoli and Michael G. Wilson, Tom Cruise, Donna Langley, Michael D. Eisner, Alan Horn, Jeffrey Katzenberg, Kathleen Kennedy, Sherry Lansing, Jack Warner, Darryl F. Zanuck and Cecil B. DeMille. THR Newsletters Sign up for THR news straight to your inbox every day
EU-banned <b>facial</b>-<b>recognition</b> cameras made in Spain now watch millions of Indians
At Howrah station in Kolkata, the trains never really stop, and neither do the cameras. One of India’s busiest railway terminals, around a million people pour through it every day, with crowds so dense that following a single face can be near impossible, at least with the human eye. Yet mounted high above the entrance and exit gates, platforms, food courts and waiting rooms, around 100 live facial-recognition cameras silently keep track. The system, installed in the past year, lifts faces from the live feed and cross-checks them against a database of photos: wanted offenders, criminal suspects, missing people. A match alerts railway police to the exact spot a person was seen, and authorises police to approach them. Some commuters seem unaware that they are being surveilled in this way. Barnali Biswas, a private sector employee who passes through six days a week, was unbothered when questioned by a reporter, saying she thought it was probably good for safety. “People with nothing to do with crime had nothing to fear,” she said. This kind of camera, enabled with facial-recognition software, is increasingly familiar across India. And in eastern India, one supplier of that software is the Spanish firm Herta Security. According to local partners and company documents, Herta supplies hundreds of railway stations across the region, Delhi’s largest prison complex, a pilgrimage site in Ayodhya, and the city control rooms of Ahmedabad. Herta confirmed the use of its technology in India but in a written statement said that it would “not disclose confidential customer information”. Its software may also be deployed at Howrah station itself, though local railway officials would not confirm this when asked. A source at one of Herta’s Indian partners told Investigate Europe that they estimated that more than 4,000 cameras across the world’s largest democracy are
Grocery Outlet's <b>facial recognition</b> rollout divides Bay Area shoppers
Grocery Outlet's facial recognition rollout divides Bay Area shoppers PLEASANT HILL – Facial recognition technology is now greeting shoppers at some Bay Area Grocery Outlet stores, and reactions to the new anti-theft tool are mixed. The Emeryville-based chain has rolled out software called SAFR at several of its Bay Area locations, including the Pleasant Hill "Bargain Market." Signs posted on the front doors alert customers that facial recognition is being used to identify people suspected of shoplifting, alerting store employees if someone on a watchlist walks inside. The rollout comes as shoplifting in California sits at an all-time high, with the most recent FBI data showing it's now nearly 50% worse than before the pandemic. For some customers, the technology makes sense. June Guerrero, who spent years managing a store, said she watched theft take a toll on retailers firsthand. "I worked for years as a manager of a store and the theft was just unbelievable," Guerrero said. "I agree with it." Others support the goal but take issue with the method. Barbara Jackson said she understands the intent but draws the line at her image being scanned. "I do understand, but invading my privacy with my picture. I don't agree on that," Jackson said. "You gotta find a better way." Shopper Steve Burdette said his concern isn't the camera — it's accuracy. "It could lead to a lot of problems, I think for companies and businesses and people," Burdette said. That question is one privacy experts are watching closely too. Mario Trujillo, a staff attorney with the Electronic Frontier Foundation, said the system scans every customer regardless of guilt. "This is a dragnet that scans everyone. Even if you've done nothing wrong, your face is being scanned," Trujillo said. "What you're essentially doing is violating the privacy rights of
Nigerian Universities Gain Global <b>Recognition</b> As Students Express Optimism #trusttvnews
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How TSA <b>Facial Recognition</b> Actually Works—and What Travelers Should Know
How TSA Facial Recognition Actually Works—and What Travelers Should Know Facial recognition is helping travelers skip lines and move through airports faster. Here’s where it’s being used and what to know before you opt in. By Emily Cappiello Emily Cappiello Emily Cappiello is a travel, food, and beverage writer covering the intersection of delicious bites, sips, and travel adventures. Her work has appeared in Travel + Leisure, Condé Nast Traveler, Departures, Vinepair, Martha Stewart Living, and more. Travel + Leisure Editorial Guidelines Published on July 14, 2026 Leave a Comment Close A woman using facial recognition at the airport. Credit: FG Trade/Getty Images It seems like everything is always changing when it comes to airport security. Shoes off or on? Laptops out or in? Hands up or down? Even I, a frequent flier, find myself inquiring about the rules these days. And as a seasoned traveler, I’ve largely surrendered to the process. Between TSA PreCheck, Global Entry, and Clear—a private company that already has my fingerprints and retinal scans on file—I’ve not thought twice about having my photo taken at the airport. But as the Transportation Security Administration (TSA) expands its facial comparison technology across airports nationwide, not every traveler is as comfortable handing over another piece of biometric data. According to the TSA, the technology enhances security while helping passengers move through checkpoints more efficiently. For some, that’s a welcome trade-off. For others, the idea of having their face scanned before boarding raises questions about privacy, consent, and exactly what happens to that information once it’s collected. IDEMIA, an authorized TSA service provider that developed the Credential Authentication Technology (CAT) system at airport checkpoints, also helped create the current facial recognition technology used today. This is designed to speed up wait times at airports while maintaining safety—an especially important
Holidaymaker needed rabies treatment after bitten by bat she rescued from pool
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Four SF Grocery Outlet Stores Are Now Using <b>Facial Recognition</b> Software at Entrances
Grocery Outlet has begun installing technology at several Bay Area locations, which stores images of customers’ faces alongside security camera footage and compares it against a “watchlist” with info about suspected criminal activity shared by other retailers. Emeryville-based Grocery Outlet has quietly introduced facial recognition technology at four San Francisco stores, including the Mission, Portola, Bayview, and Richmond districts, as part of an effort to combat shoplifting, as Mission Local reports. Small signs posted near store entrances alert customers that “Face Matching software” is being used for security purposes, similar to a system utilized by a few Castro bars to identify blacklisted customers, as SFist reported last month. According to the Chronicle, similar signs were also spotted at Grocery Outlet locations in Pleasant Hill and Concord, which direct customers to a QR code with SAFR Guard’s privacy policy. It remains unclear how many other Grocery Outlet locations use the system or how long it’s been installed. Per Mission Local, Grocery Outlet stores are leased to independent store operators. SAFR Guard, operated by the company SAFR, reportedly captures facial images of people entering stores and combines that information with security camera footage and retailer-provided information about people suspected of theft, violence, or other illegal activity. The company maintains a “watchlist” of individuals identified through previous incidents and sends alerts to retailers when someone on the list enters a store. “SAFR Guard is designed for security purposes to assist retailers in preventing unlawful conduct and maintaining a safe environment by identifying individuals reasonably suspected of engaging in activities,” the company’s privacy policy states, “such as, but not limited to, theft, fraud, violence, harassment, or other malicious, deceptive or unlawful conduct.” SAFR President Charisse Jacques told Mission Local the system is intended for “targeted deployment” rather than broad surveillance. The technology does not
Operation Sentinel Justice [<b>Image</b> 6 of 6]
Pfc. Giselle Jimenez assigned to the 61st Quartermaster Battalion, 13th Armored Corps Sustainment Command, III Armored Corps, receives a coin from Col. La'Havie Brunson in recognition of excellence during Operation Sentinel Justice at Camp Shelby, Mississippi, June 17, 2026. Jimenez was recognized for outstanding performance, professionalism, and dedication in support of the operation. (U.S. Army photo by Pfc. Belle McPherren) | Date Taken: | 06.18.2026 | | Date Posted: | 07.14.2026 16:42 | | Photo ID: | 9810550 | | VIRIN: | 260618-A-XM626-8334 | | Resolution: | 3923x2617 | | Size: | 1.75 MB | | Location: | US | | Web Views: | 6 | | Downloads: | 0 | This work, Operation Sentinel Justice [Image 6 of 6], by PFC Belle Mcpherren, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Operation Sentinel Justice [<b>Image</b> 3 of 6]
Spc. Amendrill Lewis assigned to the 61st Quartermaster Battalion, 13th Armored Corps Sustainment Command, III Armored Corps, receives a coin from Col. La'Havie Brunson in recognition of excellence during Operation Sentinel Justice at Camp Shelby, Mississippi, June 17, 2026. Lewis was recognized for outstanding performance, professionalism, and dedication in support of the operation. (U.S. Army photo by Pfc. Belle McPherren) | Date Taken: | 06.18.2026 | | Date Posted: | 07.14.2026 16:42 | | Photo ID: | 9810532 | | VIRIN: | 260618-A-XM626-3903 | | Resolution: | 4069x2715 | | Size: | 1.97 MB | | Location: | US | | Web Views: | 6 | | Downloads: | 0 | This work, Operation Sentinel Justice [Image 6 of 6], by PFC Belle Mcpherren, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
<b>Facial recognition</b> in the age of deepfakes
In this article series, we’ve taken a look at three data-intensive technologies that are giving law enforcement agencies unprecedented access to mission-critical information in the field, at the station, in the crime lab and in the real-time crime center. Added to the list — along with body-worn cameras, drones and automated license plate readers — is a lesser-used but powerful technology that has shown the potential to quickly help narrow investigative leads, locate suspects as well as missing persons and verify identities in controlled settings like correctional facilities and border crossings: facial recognition. From image comparison to investigative intelligence At its simplest, facial recognition technology compares an image of a face against a database of known images and returns possible candidates. That process can support a range of legitimate public safety needs, from verifying the identity of an unknown person to helping identify a suspect in a serious violent crime. The most useful way to understand facial recognition is as a tool for narrowing investigative leads. It does not replace an investigator, establish probable cause by itself or eliminate the need for corroborating evidence. Rather, it can help trained personnel quickly sort through a mountain of images and focus their next steps. Investigators working a violent assault may have a partial image from a security camera. A facial recognition search may return several possible candidates for review. From there, investigators still need to compare the result with other evidence, such as witness statements, location data, records checks, vehicle information or additional video. In the case of a missing person, a possible match may help investigators identify a person seen on camera at a transit station, hospital or public venue. In unidentified remains cases, facial comparison may be one piece of a broader forensic effort that also includes fingerprints, dental records,
Anthropic's newest ad is creeping people out
Anthropic is known for its creative marketing, but the AI company may have been a little bit too creative when it conjured up its most recent advertisement. Titled “There’s hope in hard questions,” the company’s latest ad has been unsettling viewers with its weird imagery and doomer-ist tone. The ad begins with a video of a burning house (not exactly a heartwarming start) before pivoting to a series of still images. These images include a crowd of people being surveilled by facial recognition, a homeless person sleeping on the street, rows upon rows of tombstones in a cemetery, and what appears to be a group of laborers toiling in a mine where (presumably) raw materials for smartphones are being dug up. Meanwhile, a voice-over track features different people asking questions like “Can AI be trusted?” and “Who’s gonna hit the brakes if we need to?” In short: Not exactly the family-friendly crowd-pleaser of the year. At the same time, it’s also not particularly far afield from the company’s past messaging. Anthropic has consistently attempted to depict itself as the ethical foil to other AI companies. This latest marketing stunt — which leans into criticism of AI as a way to make Anthropic seem aware of (and therefore distinctly worthy of) the responsibility it carries — would appear to be more of the same. Not everybody is having it, however. Sam Altman — the CEO of OpenAI, Anthropic’s chief rival — kicked off the criticism with some pithy trolling. “i thought this was satire, kept looking for the handle to be spelled c1audeai or something,” Altman posted to X on Monday. Other skeptics — many of whom seem to work in the tech industry — came out of the woodwork to remark upon Anthropic’s odd choice of imagery and tone. “Anthropic
Artificial Intelligence is Easily Fooled in the Search for Life
Humanity has many powerful tools in its cognitive arsenal. Language, abstract thinking, the theory of mind, and many others define who we are as animals. One of our most powerful tools is pattern recognition. Pattern recognition is built-in on our ground level, a foundational brick in our cognitive structure. Pattern recognition works on a basic, fight-or-flight level, letting us respond quickly to threats to our survival. It also works much more slowly and in a more focused way, as when scientists seek patterns in large collections of data. Our pattern recognition software is prone to errors. Pareidolia is the phenomenon of seeing patterns that aren't really there. We can see what looks like a face, for example, in simple rock, as with the Man in the Moon. People have seen religious figures in pieces of bread, and found meaning listening to song lyrics backwards. Pattern recognition is, arguably, the foundation of all artificial intelligence. AI can power its way through vast amounts of data much more quickly than people can, and can ferret out significant patterns. But, alas, as new research shows, AI's pattern recognition is prone to failure the same way ours is. The research shows how easily fooled AI is when given the task of detecting life, something it'll be needed for in future missions that seek life on other worlds. The research is titled "Can AI Detect Life? Lessons from Artificial Life," and will be presented in August at the 2026 Conference on Artificial Life in Waterloo, Canada. The authors are Ankit Gupta and Christoph Adami from Michigan State University. "Modern machine learning methods have been proposed to detect life in extraterrestrial samples, drawing on their ability to distinguish biotic from abiotic samples based on training models using natural and synthetic organic molecular mixtures," Gupta and Adami
A lightweight edge-enhanced detection model for small-object <b>recognition</b> in tennis match scenarios
Figures Abstract The rapid advancement of deep learning has enabled intelligent analysis in professional sports, yet tennis remains particularly challenging due to small and fast-moving objects, frequent occlusions, and complex backgrounds. To address these difficulties, we propose YOLO-Net, a lightweight detection framework tailored for tennis event analysis. Built upon YOLO11n, the framework integrates three task-oriented improvements: a C3k-MSEIS module for multi-scale edge enhancement and dual-domain feature selection to refine fine-grained boundaries; an ECA channel attention mechanism inserted after C2PSA to strengthen inter-channel dependency modeling and improve feature discriminability; and a Focaler-IoU loss function to emphasize hard and small samples while reducing localization errors. In addition, we construct and annotate a dedicated tennis dataset containing 6,648 images across three categories—player, racquet, and ball—covering diverse scenes, camera angles, and lighting conditions. Experimental results show that YOLO-Net achieves 84.5% precision and 78.2% mAP@0.5 with only 2.58M parameters, outperforming the YOLO11n baseline by 2.5% in precision and 0.9% in mAP while maintaining real-time inference. These findings demonstrate that YOLO-Net is an efficient, accurate, and deployable solution for applications such as referee assistance, tactical analysis, and intelligent broadcasting in tennis competitions. Citation: Du X, Wang T, Zu W, Dong X, Jia L (2026) YOLO-Net: A lightweight edge-enhanced detection model for small-object recognition in tennis match scenarios. PLoS One 21(7): e0335558. https://doi.org/10.1371/journal.pone.0335558 Editor: Gen Li, Nanjing Forestry University, CHINA Received: October 13, 2025; Accepted: June 20, 2026; Published: July 14, 2026 Copyright: © 2026 Du et al. 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: The validation dataset used to support the evaluation results of this study is publicly available in Kaggle at https://www.kaggle.com/datasets/taowang1123/yolo-net. The released data include
ADRD: Detecting diffusion-generated <b>images</b> via adversarial perturbation induced ...
Figures Abstract The rapid advancement of diffusion models has raised concerns about their misuse in generating deceptive visual content, motivating growing interest in AI-generated image detection. Many existing detection methods rely on image semantic features, but modern diffusion models are optimized to closely match the semantic structure of real images, reducing the effectiveness of semantic-based detection. An alternative line of work exploits differences revealed through diffusion reconstruction; however, most existing approaches treat reconstruction error as a static and passive metric, which can be sensitive to generators or post-processing, thereby limiting robustness. In this work, we propose Adversarial Diffusion Reconstruction Distance (ADRD), a detection framework that models diffusion reconstruction as a dynamic response process rather than a fixed descriptor. ADRD actively probes the reconstruction behavior by introducing perturbation in latent space and measuring how reconstruction deviations respond under identical perturbations. We empirically observe that real images typically exhibit larger and more variable reconstruction responses, while diffusion-generated images tend to show more stable reconstruction behavior, reflecting differences in their alignment with the diffusion model’s implicit data manifold. By characterizing reconstruction sensitivity instead of absolute reconstruction error, ADRD provides a complementary perspective to existing reconstruction-based detectors. Experimental evaluations on multiple benchmarks suggest that reconstruction response under controlled perturbations constitutes a meaningful signal for diffusion-generated image detection. The code is available at https://github.com/ezell-chou/adrd Citation: Zhou Y, Hu X, Tong J, Lin C, Sun M, Chen S (2026) ADRD: Detecting diffusion-generated images via adversarial perturbation induced reconstruction discrepancy. PLoS One 21(7): e0350655. https://doi.org/10.1371/journal.pone.0350655 Editor: Zeyar Aung, Khalifa University, UNITED ARAB EMIRATES Received: April 2, 2025; Accepted: May 15, 2026; Published: July 14, 2026 Copyright: © 2026 Zhou et al. 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,
Flock Traffic Cameras Could Get Innocent People Arrested. This Website Tells You Where They Are
If you are unaware, Flock is a private company that uses automatic license plate recognition (ALPR), video surveillance, image recognition, and AI to run a mass surveillance system under contract with law enforcement agencies. Understandably, there has been public pushback on a system that currently scans over 20 billion cars per month across 49 US states. That pushback garnered national attention recently when Joel Feder, director of content and product at The Drive, found out firsthand just how invasive – and mistake-prone – the system can be. The veteran auto journalist and his wife were ambushed by law enforcement in a parking lot by police that had, with Flock's help, been tracking them for days in a media vehicle he was reviewing. Its license plate had been incorrectly flagged as stolen. Pushback is finally turning into big rejections by cities. Currently, 86 cities across the US have canceled their contracts with Flock over concerns about privacy, data, and accuracy. Most recently, Los Angeles, California, has halted its use of the Flock system, and the city is part of a growing list. But what about all the cameras still in use? As they say... there's an app for that. Pressure Is Mounting... Alongside Awareness The biggest resource currently available for concerned drivers and privacy advocates is DeFlock – an open-source project mapping license plate readers that's available through its website or phone app. DeFlock can show how many Flock cameras are in an area and where they are, and the app includes a tool that flags city meetings that have Flock on their agenda, so people can be notified, show up, and be heard. "The sticking point is around having very clear terms about who owns the data, what happens with the data once they collect it." - Dean Gialamas, LAPD