AUSTIN, Texas, June 30, 2026 (GLOBE NEWSWIRE) -- Pensa Systems, the industry’s first scalable Vision AI solution for shelf planning and in-store execution, today announced two newly issued, foundational AI patents that address some of the biggest barriers to deploying AI, at scale, in physical retail. Retail stores present a difficult challenge for any AI system. A typical grocery or general merchandising store carries 30,000-50,000 discrete products, many of them visually similar on the shelf. Shelf assortments and layouts are historically planned at headquarters by hand but rarely match what is actually in the aisle. Today, retail associates and brand representatives still maintain and audit shelves largely by hand and by barcode-scanning of individual items one at a time. This activity is tedious and error-prone, but necessary to check for stockouts, misplaced items and to ensure merchandising, promotional displays and pricing are set properly. Early computer vision and AI approaches struggled to keep up with the constant packaging changes and product turnover, producing high inaccuracy rates and the confident-but-wrong outputs now widely known as AI “hallucinations” within Large Language Models (LLMs), and in earlier digital image recognition technologies. The common workaround has been to quietly put remote workers behind the scenes, often referred to as Human in the Loop (HITL), to correct the AI before its data or actions are released. That approach is expensive, still inaccurate, and causes crucial lags precisely where immediate, trustworthy AI would have its largest impact: in the store, during execution. “AI that hallucinates isn’t just wrong, it destroys the trust of those who depend on it,” said Richard Schwartz, President and CEO of Pensa Systems. “Retail is a complex environment. Getting AI right, at scale and exactly when and where it is needed is not optional. The foundational work signaled by these patents is
Jun 30, 2026 · via markets.businessinsider.com
Abstract Cardiovascular complications associated with Post-COVID-19 Patients remain difficult to identify at early stages due to heterogeneous physiological manifestations and the limited integration of imaging and clinical indicators in conventional diagnostic frameworks. To address this challenge, this work proposes a multimodal deep learning framework for cardiovascular disease (CVD) risk prediction by integrating cardiac computed tomography (CT) imaging with structured clinical data. The proposed approach applies Adaptive Bilateral Filtering for image enhancement, Kernel Density Fuzzy C-Means (KDFCM) for myocardial segmentation, and Squeezing Extract Chirplet Transform (SSECT) for extracting multi-scale texture and frequency-based imaging features. Clinical variables are preprocessed through Z-score normalization, interquartile range (IQR)-based outlier removal, and Isolation Forest anomaly detection, followed by Adaptive Starfish Optimization (ASO) for feature selection. Imaging and clinical representations are integrated through feature-level fusion and classified using a Deep Image Recognition–based Generative Adversarial Network (DIR-GAN), where adversarial learning enhances discriminative feature representation and model robustness. Experimental evaluation demonstrates strong predictive performance, achieving 94.98% accuracy, 94.98% sensitivity, 93.38% specificity, 95.43% precision, and 94.74% F1-score. The findings indicate that the proposed framework provides reliable and clinically relevant cardiovascular risk stratification for patients recovering from COVID-19, while demonstrating robustness against heterogeneous multimodal data. Similar content being viewed by others Funding This research received no external funding. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Ethical approval and informed consent This work did not involve direct interaction with human participants or the collection of new patient data. The imaging dataset was obtained from the publicly accessible COVID‑19 CT Scan Dataset (https://www.kaggle.com/datasets/ahmedtronic/covid-19), while structured clinical records were obtained from the UCI Machine Learning Repository – Heart Disease Dataset (https://archive.ics.uci.edu/dataset/45/heart+disease). All datasets used were publicly available and fully anonymized; therefore, additional ethical approval and informed consent were not required in accordance with applicable
Jun 30, 2026 · via nature.com
The South Korean government will introduce stringent regulatory measures to curb the proliferation of "dae-po" phones—illegally registered mobile devices increasingly linked to voice phishing and financial crimes. The Ministry of Science and ICT (MSIT) announced on June 30 a comprehensive policy framework designed to prevent the illegal use of mobile phones. The measures build upon a government-wide crackdown on telecommunications-enabled fraud initiated last year. According to MSIT, the policy establishes tailored preventive frameworks based on specific types of device misuse, while strengthening post-incident enforcement and legal penalties. The shift reflects the expanding role of smartphones as primary tools for financial transactions and digital identity verification. To prevent identity theft, the government will mandate facial recognition authentication at the point of mobile phone activation. Following a pilot phase conducted last year, the system will be introduced sequentially starting July 6. The finalized implementation plan incorporates guidelines from the Personal Information Protection Commission (PIPC) and the National Human Rights Commission of Korea (NHRCK). During the initial rollout, users opting for facial recognition must complete the verification step within three attempts to proceed with line activation. If facial recognition fails due to technical errors, alternative identity verification methods—such as mobile identification apps or physical identity certificates issued on the same day—will be provided. In October, the government plans to codify the legal grounds for facial recognition by amending the Enforcement Decree of the Telecommunications Business Act. By November, a subscription restriction service, which previously required manual opt-in by users, will be applied by default during contract signing. For foreign nationals, MSIT will collaborate with the Ministry of Justice to upgrade verification infrastructure and strictly enforce a "one person, one line" rule for prepaid SIM cards. The framework also targets "identity lending"—the illegal transfer of mobile lines. To curb fraudulent financial schemes where vulnerable
Jun 30, 2026 · via thelec.net
S. Korea to begin facial verification for new mobile phones next month SEOUL, June 30 (Yonhap) -- South Korea will implement facial recognition technology starting next month as a way to strengthen the identity verification process during mobile phone registrations, the government announced Tuesday. The Ministry of Science and ICT unveiled the plan during a briefing in Seoul as part of broader efforts to prevent illegal activities, including mobile identity theft, when registering new mobile phone numbers. Starting Monday, customers registering a new mobile phone number will be able to choose either face recognition or other government-approved forms of identification, such as resident registration certificates, to verify their identity. Face recognition was made optional, amid concerns over infringements of basic rights, the ministry said. Raw facial images will be discarded immediately after the verification process is completed to prevent potential data leaks, it added. The government plans to expand the range of identity verification options while revising relevant laws by October to establish the legal basis for introducing facial recognition services. Last year, the ministry announced a plan to make face recognition mandatory for new mobile phone registrations, sparking public concerns amid a series of data breaches in local mobile carriers. State data protection and human rights authorities pointed out that the plan lacked a sufficient legal basis and could infringe on people's fundamental rights. fairydust@yna.co.kr (END) - (World Cup) S. Korea eliminated from group stage - At Jeju Forum, U.N. chief candidates vow to reclaim relevance - U.S. nuclear expert calls for patience, caution on Seoul-Washington nuclear submarine cooperation - Security must extend beyond defense in fragmented world: ex-U.S. ambassador - Former leaders call for renewed cooperation amid fragmented global order - At Jeju Forum, U.N. chief candidates vow to reclaim relevance - (World Cup) S. Korea eliminated from
Jun 30, 2026 · via en.yna.co.kr
XAILIENT ANNOUNCES SUCCESSFUL INDEPENDENT VALIDATION OF CASINO EYE-D BY GAMING LABORATORIES INTERNATIONAL (GLI) LAS VEGAS, June 29, 2026 /PRNewswire/ -- Xailient, a leader in Edge AI-powered Facial Recognition (FR) solutions for the gaming industry, today announced that Gaming Laboratories International (GLI) has conducted a successful independent validation of the company's ground breaking Casino Eye-D FR platform. GLI's evaluation confirms the functional and technical specifications of Casino Eye-D, which integrates with the industry's leading casino management systems (CMS) to revolutionize patron identification, loyalty engagement, and AML monitoring across gaming floors. "GLI's validation represents an important milestone for Xailient and the broader gaming industry," said Lars Oleson, CEO of Xailient. "As casinos adopt AI-driven technologies to improve guest experiences and operational efficiency, independent validation provides operators, regulators, and technology partners with full transparency and confidence in these technologies' functionality." Key capabilities reviewed during the evaluation included: - Patron identification and recognition integrated with casino management systems - Automated enrollment and image quality assessment - Enterprise-scale biometric processing and identity management - Support for loyalty engagement and player recognition workflows - Excluded patron identification and security alerting - Secure API-based integration with existing CMS platforms - Scalable deployment architecture designed for casino environments - Confirmed exclusive functionality with CMS and no engagement with any game functionality Doug Beavers, Sr. Director of Business Development said, "GLI's validation provides another step to market deployment of our 'AI with an ROI' facial recognition technology to the gaming floor. We are excited by the support from regulators, our CMS partners, and casino operators as we launch the transformative Casino Eye-D technology to the gaming floor." Xailient has previously announced integrations with Konami Gaming's SYNK Vision™, Light & Wonder CMS and is coordinated with operators, regulators, and industry stakeholders to advance the adoption of AI-powered guest recognition solutions
Jun 30, 2026 · via prnewswire.com
Abstract Simultaneous Localisation and Mapping (SLAM) is a fundamental building block for markerless Augmented Reality systems. However, most conventional SLAM systems exhibit poor performance in real-time environments because of the influence of dynamic objects in unstructured surroundings. We are proposing a new Dynamic Visual Inertial SLAM system called DVI-SLAM to address the challenges of dynamic content presented in complex urban scenes. Leveraging instance-aware segmentation and optical flow methods, our system can detect and exclude actively moving objects from the tracking process. In addition to excluding features of moving objects from the pose estimation process, the system also detects, tracks, and generates static-map of the surrounding environment. This helps develop a robust AR system or mobile robot system to effectively handle complex outdoor urban scene scenarios. The proposed DVI-SLAM framework was tested on the TUM RGB-D and TUM-VI datasets as well as in real-world environments. The results showed an improvement in ATE’s Root Mean Square Error (RMSE) up to 24.80 and 32.34% compared with ORB-SLAM3, on monocular-inertial and stereo-inertial sequences of the TUM-VI dataset, respectively. Similarly, an improvement up to 64.89 and 73.75% was achieved for real-time monocular and RGB-D inertial sequences of real-time environments. Further, the sequence with a lot of dynamic content, such as the TUM-RGBD dynamic dataset, showed up to 99.4% improvement in ATE RMSE. The results demonstrated that DVI-SLAM significantly enhances Absolute Trajectory Error accuracy as compared to the state-of-the-art ORB-SLAM3 method. Extensive experiments evaluation shows that DVI-SLAM performs robustly across both static and dynamic scenarios, while maintaining competitive performance in static and low-dynamic scenes, it delivers significant improvements in highly dynamic environments. Similar content being viewed by others Introduction In Augmented Reality (AR) systems, accurate registration of virtual content with respect to the physical environment is a fundamental requirement for achieving stability and immersive user experience.
Jun 29, 2026 · via nature.com
Abstract Terahertz (THz) imaging technology is widely used in applications such as security screening, radar detection, and biomedical applications. Nevertheless, due to the inherent limitations of imaging conditions, THz images often exhibit low contrast, blurred contours, and reduced feature information under partial occlusion, which significantly compromises recognition accuracy. To address these challenges, we proposed an Enhanced and Occlusion-aware Focus YOLOv5(EOF-YOLOv5), an improved architecture based on YOLOv5. In this study, image enhancement preprocessing was applied to raw THz image datasets acquired from a THz active array imaging system to improve target contrast and contour clarity. An Occlusion Aware Context Attention (OCA) mechanism was integrated into the neck network of YOLOv5. This mechanism dynamically adjusts attention weights and enhances feature responses in visible areas by capturing spatial occlusion patterns via 1 × 1 convolution and modeling inter-channel dependencies. Additionally, the original Complete Intersection over Union (CIoU) loss function was replaced with the Focal-Efficient Intersection over Union (Focal-EIoU) loss function to reduce excessive focus on simple samples and improve the learning performance for challenging samples. Experimental results demonstrate that image enhancement preprocessing significantly improves both visual quality and structural information, thereby boosting the network’s recognition accuracy. On the same preprocessed dataset, the EOF-YOLOv5 algorithm outperforms the baseline YOLOv5 model, elevating precision (P) from 66.7 to 79.3%, recall (R) from 73.1 to 80.6%, and mean average precision (mAP50) from 75.5 to 83.7%. The proposed model effectively identifies targets under blurry and occluded conditions, providing an innovative solution for terahertz image detection. Similar content being viewed by others Funding This work was financially supported in part by the Key Scientific Research Plan of Education Department of Shaanxi [23JY035], The Youth Innovation Team of Shaanxi Universities [K20220184], and Natural Science Foundation of Shaanxi Province [2025JC-YBMS-744]. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests
Jun 29, 2026 · via nature.com
Can Clothes Make You Invisible to Facial Recognition? Does life feel Orwellian sometimes? One researcher has a solution for you: graphic tees that confuse the neural networks in surveillance cameras. About 10 years ago, an app developer named Hoan Ton-That scraped your social media photos. You didn't know it at the time, but he added your photos to a database, and used it to build a facial recognition platform called "Clearview AI." Your face has been in his database ever since. In the years since, Clearview has only collected billions more photos, attracted investment from plutocrats, and been rewarded with multimillion-dollar contracts from law enforcement agencies across America like Immigration and Customs Enforcement (ICE). First in cautionary science fiction, and then in our timeline, facial recognition has almost always been trained and imposed on regular citizens without their consent. Clearview AI tacitly understood that, and today it's official US government policy. In an analysis of ICE's other facial recognition toy, "Fortify," the Department of Homeland Security (DHS) acknowledged that "ICE does not provide the opportunity for individuals to decline or consent to the collection and use of biometric data/photograph collection." "The surveillance state has overreached," says Bill Swearingen, aka @hevnsnt. "You never opted into this, and there's no way to opt out. That's why I'm trying to give power back to the people a little bit." At Black Hat USA 2026 next month, Swearingen will debut his proposed solution for our totalitarian nightmare: clothing. Graphic hoodies, shirts, scarfs, etc., with patterns that demonstrably confuse or outright break current facial recognition artificial intelligence (AI). The Weak Point in Facial Recognition In the gestalt, facial recognition is an unseen fabric pervading all public spaces. The first step in beating it is making it a smaller, knowable thing. Any given facial recognition flow
Jun 29, 2026 · via darkreading.com
Wearable digital stethoscopes are transforming remote healthcare by enabling continuous monitoring of heart and lung activity outside traditional clinical settings. UNSW researchers developed the AusculPatch, a lightweight wearable sensor that detects subtle mechanical vibrations from the heart, lungs, blood flow, and pulse waves while users go about their daily routines. Unlike conventional wearables that primarily track heart rate or blood oxygen, the patch captures detailed physiological signals and is designed to work with AI systems that can identify abnormal patterns and notify clinicians before symptoms become severe. For healthcare providers, continuous home monitoring could improve chronic disease management while reducing unnecessary hospital visits and enabling earlier intervention. Medical device companies also have opportunities to expand beyond fitness tracking into clinically focused wearable diagnostics that generate richer health insights. As AI-driven analysis becomes more sophisticated, wearable digital stethoscopes could support more personalized care, improve remote patient monitoring, and help healthcare systems manage growing patient populations more efficiently. Image Credit: UNSW Key Themes Behind This Trend - Continuous Acoustic Monitoring - Wearable sensors that capture heart, lung, and vascular sounds create new possibilities for detecting clinical changes during everyday routines rather than brief appointments. - AI-driven Diagnostics - Advanced pattern recognition can turn subtle physiological vibrations into predictive health insights that support earlier intervention and more personalized patient care. - Home-based Chronic Care - Remote monitoring tools expand chronic disease management beyond hospitals and clinics, reducing avoidable visits while giving providers richer longitudinal health data. Where This Applies - Medical Devices - Clinically focused wearables represent a shift from basic fitness tracking toward regulated diagnostic platforms that generate deeper cardiovascular and respiratory intelligence. - Remote Healthcare - Virtual care models benefit from continuous physiological data streams that help clinicians assess patient status without relying solely on scheduled consultations. - Health AI -
Jun 29, 2026 · via trendhunter.com
Can generative AI be an ally in rooting out ransomware threats? A UC researcher suggests rethinking ways to catch bad actors The online publication, Securities.io, reports that ransomware attacks on business activity are projected to exceed $265 billion annually by 2031. Using generative AI to find a way to help alleviate these cybersecurity threats may be a smart option. Securities.io cites recent research published by Nelly Elsayed, associate professor in the UC School of Information Technology, in the Journal of Information Security and Applications, which suggests that generative AI may be an ally in strengthening ransomware defense. “We are in a hype era of AI,” says Elsayed, founder and leader of the Applied Machine Learning and Intelligence Lab within the College of Education, Criminal Justice, and Human Services. “Some people support it, others fear it, but in general people who design technology are trying to use it for good.” Elsayed's research article argues that generative AI can be used to integrate synthetic data generation and behavioral forecasting, stress test systems by checking for adversarial behavior simulation and improve trust of human-AI collaboration in security operation systems. Cybersecurity analysts and system defenders can use AI to detect new malicious attacks and classify and identify new means of attack from bad actors, according to Elsayed. Simulating with hackers might allow for creating possible attack scenarios and learning to think like attackers to offer more robust tools for defense, she adds. “It’s a way to generate a combination of possible attacks system defenders might not have considered,” she says. Elsayed adds a practical example could be a user pasting a suspicious email into a generative AI system and asking about the validity of the email. AI could help screen and catch red flags: a suspicious logo or misspellings. “AI can become an early
Jun 29, 2026 · via uc.edu
CardSight AI Supports Identifying Multiple Cards in One Image Custom-trained computer vision meets collectors where they are â identifying card a full binder page, while legacy tools still scan one card at a time We train our identification for the real-world conditions collectors actually shoot in, not lab conditions, and our AI is built to read several cards in a single image. — Signe Bone, Founding Engineer at CardSight AI PORTLAND, ME, UNITED STATES, June 29, 2026 / EINPresswire.com/ -- CardSight AI, the computer-vision platform powering trading card identification for developers, marketplaces, and hobbyists, today launched "Break Out of the Box," a campaign built around a core advantage of its technology: users never have to line a single card up inside a guide box and scan it one at a time, like depositing a check in a banking app. With CardSight AI, a single photo is identified in one shot, whether it's taken at the card show, the local shop, or the table where you sort your collection, at any angle and in any light, even a full nine-card binder page. As competition intensifies, the campaign draws a deliberate line between CardSight AI's purpose-built approach and the generic techniques many competing tools still rely on. The problem with scanning "in the box" Most card-identification tools depend on legacy image-matching techniques such as perceptual hashing (p-hashing) and k-nearest-neighbor (kNN) matching. These methods compare a new photo against a library of reference scans by measuring how visually similar the two images are. That only works when the new photo closely mirrors the reference image, which forces the card to be flat, centered, evenly lit, and captured one at a time. The result is what CardSight AI calls the "deposit-a-check" experience: hold the card inside an on-screen box, keep it still, wait for
Jun 29, 2026 · via tech.einnews.com
New spying threats force rethink of biometric identity checks The Five Eyes intelligence alliance’s warning this month that Chinese intelligence services are using fake recruiters on LinkedIn and other job platforms to cultivate people with access to sensitive information is a reminder that the most consequential security failures often begin before a system ever performs a check. Made up of Australian, Canadian, New Zealand, UK, and U.S. intelligence agencies, their joint bulletin, Safeguarding Our Secrets, describes how Chinese intelligence officers or their affiliates pose as recruiters, consultants, and representatives of credible-appearing companies to identify people with access to government, military, economic, or policy information. Publicly available information can also be coupled to the de-anonymization of information obtained through data brokers. De-anonymization occurs when data that has been stripped of direct identifiers, such as names or email addresses, is combined with other datasets to re-identify individuals. All of which is designed to turn a real person into an insider. A job offer provides the opening. A remote interview creates rapport, and the request for something like a trial analytical report tests the target’s willingness to provide information. The demands then become more sensitive. But the episode also illustrates a wider problem emerging across digital identity systems. Security may be built around sophisticated facial recognition, document authentication, and biometric matching, yet these tools can do only so much when the person, the credential, or the digital stream reaching the system has already been manipulated. A biometric scanner can compare a face with a stored image, determine whether a fingerprint resembles a template on file, and can confirm that the person in front of a camera resembles the person associated with a passport or account, but what it cannot automatically establish is whether the identity entered into the system was genuine at the
Jun 29, 2026 · via biometricupdate.com
Artificial intelligence (AI) gained significant public attention when IBM's Deep Blue defeated the world chess champion in 1996 and 1997. In 2011, IBM's Watson surpassed human champions on Jeopardy, illustrating the integration of curated human expertise into computer systems through the analysis of thousands of grandmaster games and consultation with chess professionals and Jeopardy winners. By 2022, generative AI software based on foundational models had become prevalent, supported by increased computing power and extensive training data derived from photos, comments, and captions shared on social media. This growth in software capabilities enabled diverse AI applications, including: - Natural language processing - Image recognition - Face recognition - Autonomous driving - Speech recognition - Robotic AI Millions of mobile phone users depend on AI-powered software for various tasks. Virtual and voice assistants such as Alexa, Siri, and Gemini are widely utilized for daily activities both inside and outside the home, including remote operations. During travel, individuals encounter AI technologies used by public officials for image and face recognition. Employees in manufacturing facilities, regardless of size, also employ classical AI within controlled production environments. A recent Microsoft report indicates that artificial intelligence usage is increasing annually. The latest Microsoft Education survey, which included grade schools through universities, found that 90% of education leaders, educators, and students have used AI at least once for school-related purposes. The report further notes that institutions are leveraging AI to enhance learning and provide recommendations to assist educators and administrators. Notable examples include the Catholic University of Chile in Santiago, which deployed 194 AI pedagogical agents to support learning and improve the teaching experience. Among the most active agents, students averaged 13.2 minutes of sustained engagement, suggesting in-depth exploration of course content rather than superficial queries. In Broward County, Florida, the district implemented 20,000 Microsoft 365 Copilot
Jun 29, 2026 · via blackengineer.com
It’s not uncommon to feel pangs of regret after a big weekend out — for overindulgences of drinks, dalliances, burritos, or all of the above. Now some hungover bargoers can add “turned over personal information to surveillance companies” to the list. A number of destinations in the Castro, including The Mix Bar, Badlands, and Toad Hall, have been using the third-party security service Patronscan to scan IDs, photograph faces, and store personal information in a shared database, as first reported by Gazetteer SF. (opens in new tab) The extra level of security is meant to give businesses a way to flag problematic patrons and share that information with other bars — a kind of mass joint “86’d” list. However, the technology has concerned community members and privacy organizations. “We advise San Franciscans avoid such bars until they remove the facial recognition technology to ensure safety for the queer and trans community, free from harmful surveillance,” digital rights advocacy group Fight for the Future wrote in a post (opens in new tab) ahead of Pride weekend. The good news is that California’s privacy laws, stronger than those of most states, give you some control over what happens to that information. You can see it, fix it, delete it, and, if you feel you were incorrectly flagged by a bar using the software, dispute it. What they have and where it’s shared Before you go into a bar, look around the security check. Is there a small camera pointed at you or a posted disclosure about a scanning system? If they are using Patronscan or similar technology, you can ask about opting out, but the bar is within its rights to refuse you entry. “It’s a really unfair choice, especially for people in more vulnerable populations,” said Hayley Tsukayama, director of state
Jun 29, 2026 · via sfstandard.com
BuzzFeed GamesIf You Can Solve This Color Puzzle In Less Than 3 Minutes, You Have Perfect Color VisionHuedoku #54! New week, new puzzle — let’s see what you’ve got. 🌈🧩Posted 10 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 #55 — 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 29, 2026 · via buzzfeed.com
Man arrested by police using facial recognition
- Published
A man was arrested after live facial recognition technology was used in Peterborough city centre.
Cambridgeshire Police used the system in the city for the second time earlier this month.
Of the 22,000 faces scanned on 19 June, two came up as matches against a watchlist, the force said.
The man who was arrested had failed to appear in court on suspicion of driving while disqualified.
The system was first used in Peterborough on 19 May, when police scanned 34,000 faces in six hours.
Two men who were wanted for failing to appear in court were arrested on that occasion â one accused of theft and the other of shoplifting.
Cambridgeshire Police said images of people who did not match the database were permanently deleted straight away.
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Jun 29, 2026 · via bbc.co.uk
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Jun 29, 2026 · via yougov.com
Vadzo Imaging Launches AR0521 USB Camera for Interactive Digital Signage and Real-Time Gesture Recognition Applications Vadzo Imaging's Falcon-521CRS is a 5MP USB 3.0 color camera built on the Onsemi AR0521 sensor, delivering low noise rolling shutter imaging with full UVC compliance for interactive kiosk systems, digital signage displays, and real-time gesture recognition applications without custom driver development. FORT WORTH, Texas, June 29, 2026 (Newswire.com) - Vadzo Imaging today announces the Falcon-521CRS, a 5MP USB 3.0 color camera built on the Onsemi AR0521 sensor and designed for OEM engineers developing interactive digital signage platforms, touchless kiosk systems, and vision-based gesture recognition pipelines. The Falcon-521CRS delivers 5-megapixel color imaging at 2592 x 1944 resolution over USB 3.0 with full UVC compliance and a low noise rolling shutter architecture that targets applications where image quality in variable ambient light is a primary engineering constraint. The Engineering Challenge in Interactive Kiosk and Gesture Recognition Systems Interactive digital signage and self-service kiosk platforms are among the most demanding deployment categories for embedded vision camera devices. These systems operate in retail environments, transit hubs, banking terminals, and public information displays where lighting is rarely controlled, and the subject population spans users of different heights, skin tones, and hand positions. A gesture recognition pipeline needs to reliably detect and classify hand shape and motion in real time across this range of conditions without generating false triggers from background movement or light variation. The image sensor sits at the front of this problem. A sensor with poor low-light noise characteristics produces images where hand contours are lost in noise floors below a certain luminance threshold. A sensor with an inadequate dynamic range causes hand tracking algorithms to fail when a user is backlit by a window or a large display panel. A camera module that requires custom driver
Jun 29, 2026 · via newswire.com
The Prospect MAX runs more reliably than most of the fleet, which is why so many people on the east side build their mornings around it. The woman heading to a hospital shift, the teenager riding to school, the man going to dialysis. This fall, if the Kansas City Area Transportation Authority gets its way, every one of them will have their face scanned by an artificial intelligence system the moment they board, and run against a watchlist before they reach their stop. What Kansas City is building, just months or years ago we likely would have described as dystopian. Kansas City wants to be the first, and that is exactly why the rest of the country is watching. “The idea of running face recognition on a camera that is pointed on live spaces in public is a line that until recently has never really been crossed in the last 25 years,” – Jay Stanley, senior policy analyst at the American Civil Liberties Union, told the Associated Press The World Cup Was the Excuse for Scanning Riders’ Faces KCATA tried this once already. It told the public the cameras were about the World Cup, about finding missing people and stopping trafficking during a tournament that draws the whole world to the city. As we know, the tournament is here right now, the matches are being played this month, and the cameras are not on the buses. The reason the agency gave the public (that it is needed for the World Cup) will be over before the cameras go up, yet, KCATA wants to return in the fall with the program more than three times its original size, up to thirty buses. When the program was first announced, the KCATA Board of Commissioners was informed at a finance committee meeting, rather
Jun 29, 2026 · via kansascitydefender.com
Georgia‘s Interior Ministry uses Polyface, a facial recognition system developed by the sanctioned Russian company Papillon AO, to identify and monitor participants in protest rallies, according to the international investigative outlet AlgorithmWatch. According to the publication, the system remains under Russian jurisdiction. It says this increases the risk that Russian security services could gain access to the biometric data of Georgian citizens, posing a threat to both Georgia’s national security and civil activists. AlgorithmWatch notes that Papillon AO is under sanctions imposed by Switzerland, Ukraine, Japan and the United States. According to the publication, Russian law enforcement agencies and countries closely aligned with Moscow, including Tajikistan, Turkmenistan, Kazakhstan and Belarus, primarily use the company’s technology. “Russia, which now supplies Georgia with surveillance technology, invaded the country in August 2008 and subsequently recognised South Ossetia and Abkhazia as independent states,” the publication says. According to the report, authorities have upgraded the Polyface system five times over the past 11 years. In October 2024, they also granted it an indefinite licence. The 2018 upgrade contract required Russian specialists to train operators from Georgia’s Interior Ministry directly. AlgorithmWatch also reports that in early June 2025, Georgia’s Interior Ministry purchased the latest software update, Polyface 3.7.0. The publication says this version relies on an algorithm developed by the Russian company 3DiVi, which is based in Novosibirsk and receives financial support from a Russian state fund. According to the report, the software can capture high-resolution images of crowds numbering in the thousands, even in low-light conditions. It can also identify individuals wearing masks or with partially covered faces. Drawing on procurement documents, AlgorithmWatch also reports that Georgia’s Interior Ministry has removed the existing limit on the number of system operators. Until 2025, no more than 30 operators could use the platform at the same time.
Jun 29, 2026 · via jam-news.net