India is deploying facial recognition and algorithmic policing tools at scale without a governing framework, where unaddressed opacity and discriminatory error risk making efficiency come at the cost of justice Artificial intelligence and biometric tools have moved from proposal to practice in Indian law enforcement, a shift that has been underway since at least 2017-2018 and has only gained urgency over time. After the 2020 Northeast Delhi riots, investigators turned to facial recognition technology to identify those involved. The results were uneven. In one case, a man spent four and a half years in custody before securing bail. His detention rested substantially on an 80 percent similarity score generated by Facial Recognition Technology (FRT) from a CCTV frame. Delhi Police deployed FRT in more than 750 riot investigations, yet over 80 percent of those that reached a verdict ended in acquittal or discharge. The state has structural reasons to lean on automation. The India Justice Report records a police-to-population ratio stalled at 155 per 100,000, well short of the United Nations benchmark of 222. Bihar manages roughly 81 per 100,000, and 22 percent of sanctioned posts remain vacant nationally. In an environment of chronic under-resourcing, algorithmic assistance is easily presented as an administrative remedy. These methods are far from foolproof; over-dependence risks blind policing. Algorithmic policing in India has expanded without an anchoring statute. The Project Panoptic tracker documents 170 facial recognition systems commissioned across agencies, though only around 20 are operational, at a cumulative outlay of ₹1,513 crore. Punjab’s PAIS searches over 390,000 records and 84,000 voice samples, Uttar Pradesh’s Trinetr holds more than 900,000 records, and Telangana issues TSCOP units to officers for real-time biometric matching. In 2018, under the Delhi High Court’s direction in Sadhan Haldar v. NCT of Delhi, the police acquired FRT for a single
May 30, 2026 · via orfonline.org
Plant Identifier Turns A Photo Into Instant Botanical Knowledge Ellen Smith — May 29, 2026 — Tech References: apps.apple Walking past unknown plants and trees often leaves people curious but without answers -- Plant Identifier removes that uncertainty by instantly identifying plants from a simple photo. Users can snap a picture or upload an image from their gallery, and the AI quickly analyzes it to determine the plant species. This makes it easy to learn about nature in real time without needing expert knowledge. The app acts like a pocket botanist, helping users understand the environment around them with minimal effort. It turns everyday walks into opportunities for discovery and learning. Plant Identifier is aimed at nature enthusiasts, gardeners, and curious users. By combining image recognition with botanical data, it makes plant identification fast and accessible. Image Credit: Plant Identifier Users can snap a picture or upload an image from their gallery, and the AI quickly analyzes it to determine the plant species. This makes it easy to learn about nature in real time without needing expert knowledge. The app acts like a pocket botanist, helping users understand the environment around them with minimal effort. It turns everyday walks into opportunities for discovery and learning. Plant Identifier is aimed at nature enthusiasts, gardeners, and curious users. By combining image recognition with botanical data, it makes plant identification fast and accessible. Image Credit: Plant Identifier Trend Themes - Real-time Visual Recognition — Instant image-based identification of plants enables automated, context-aware information delivery that can redefine mobile learning and field diagnostics. - Augmented Botanical Education — By overlaying species data and ecological facts onto real-world views, the learning experience for amateurs and students can shift from classroom-bound to experiential and location-specific. - Citizen Science Data Integration — Crowdsourced plant observations from many
May 30, 2026 · via trendhunter.com
Xactus, a fintech company that provides credit verification services for the mortgage industry, has acquired Mortgage Credit Link (MCL) from MeridianLink, a platform designed to streamline order fulfillment for credit and data verification. Now called XedaLink, the business segment will be structured as a subsidiary of Xactus but will continue to operate within its existing framework and will keep its distinct client and partner base intact. Pennsylvania-based lender-servicer Newrez announced the launch of Rezi Mortgage Assistant, an AI-powered mortgage guide built directly into ChatGPT. The tool is crafted to provide plain-language mortgage and home equity responses to consumers, with the AI model trained on Newrez’s underwriting guidelines, lending policies and educational content. Newrez also announced a partnership with Matic to integrate its digital insurance marketplace into Newrez’s HomeHub portal. The integration is designed to enable Newrez customers to compare personalized homeowners insurance options and receive proactive quotes. GO Mortgage announced the launch of a third-party origination channel. The wholesale platform will be led by Rob Saunders, who has been named executive vice president of TPO production. The company noted that the platform will be rolled out to a select group of broker partners over the next two months, a move designed to evaluate performance at scale before expanding nationally. Planet has expanded its offerings to include non-agency loans, including a suite of non-qualified mortgage (non-QM) products featuring alternative income and debt-service coverage ratio (DSCR) options. The Connecticut-based lender had previously introduced a non-agency correspondent pilot at the tail end of 2025, noting in a recent press release that it has seen “significant adoption from its nearly 800 lending partners.” Panorama Mortgage Group, which celebrated its 20th anniversary this month, has rebranded as SimplyPMG. The move unifies the Alterra Home Loans and Travisa Financial channels under a single brand, with the
May 29, 2026 · via scotsmanguide.com
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May 29, 2026 · via youtube.com
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May 29, 2026 · via instagram.com
JACKSONVILLE, Fla. — The Jacksonville Sheriff’s Office is facing renewed questions over its use of artificial intelligence facial recognition technology after a second man said the system wrongly identified him as a suspect, leading to three months behind bars. Only on Action News Jax, Jalil Richardson, a Charlotte, North Carolina father of 10, said he was extradited to Jacksonville and spent nearly three months in jail for a car theft investigators later determined he did not commit. >>> STREAM ACTION NEWS JAX LIVE <<< “It’s overwhelming and it’s devastating and it’s outrageous,” Richardson told Action News Jax. According to a Jacksonville police report, investigators used Automated Facial Recognition, or AFR, to compare surveillance footage from a Publix parking lot theft case to Richardson’s photo. Jasmine Jackson, Richardson’s wife, said officers told them the software identified her husband as an “85% match.” “He said, ‘Jalil came back as close as 85% and that’s the reason why he charged Jalil with the crime,’” Jackson said. The investigation began April 2, 2025, when a victim told JSO he unknowingly purchased a stolen car from a man he met at a Publix on Baymeadows Road. Police later showed the victim a photo lineup, where he identified Richardson as the suspect. But timecards later proved Richardson was at work in North Carolina when the alleged crime happened nearly 400 miles away in Jacksonville. “None of the people that she had even came close to looking like my husband,” Jackson said. Richardson said he first learned about the warrant after calling police to his home in Charlotte for an unrelated disturbance. “When they arrived, they informed me they had a warrant for me out in Jacksonville and I was incarcerated for 33 days in Mecklenburg County,” Richardson said. “They extradited me to Florida after 33 days
May 29, 2026 · via actionnewsjax.com
May 29, 2026 Ice Hockey Video Analytics Using Deep Learning Title: Ice Hockey Video Analytics Using Deep Learning Presenter: David A. Clausi, University of Waterloo Date: June 3rd, 2026 at 10:30am to 11:30am Location: ENC 201 Ice hockey presents a uniquely challenging environment for computer vision and video analytics due to its high player density, rapid motion, frequent occlusions, complex interactions, and continuous gameplay. In this talk, I will present a research program focused on advancing automated understanding of ice hockey through modern video analytics techniques. The presentation will cover methods for homography, multi-object detection and tracking, player and puck localization, action recognition from broadcast video. Emphasis will be placed on the challenges associated with unconstrained sports video, including camera motion, scale variation, severe occlusion, and limited annotated training data. This work demonstrates how advances in AI and video understanding are enabling new forms of automated sports intelligence and opening opportunities for impactful interdisciplinary research collaborations. -- David A. Clausi (PEng, FCAE, FEIC, FIEEE) is a Professor in Systems Design Engineering and University Research Chair at the University of Waterloo who specializes in the field of Intelligent Systems. After earning his Ph.D. (1996) he worked in medical imaging at Mitra Imaging (Waterloo). He started his academic career in 1997 as an Assistant Professor in Geomatics Engineering at the University of Calgary. Dr. Clausi was the Associate Dean - Research & External Partnerships in the Faculty of Engineering (2018-2024). Prof. Clausi has many contributions, conducting research primarily in remote sensing, computer vision, image processing, and sports analytics. He has published extensively, has been an Associate Editor for leading journals, received many scholarships, paper awards, research and teaching excellence awards and his efforts have led to successful commercial implementations.
May 29, 2026 · via ucalgary.ca
This is part three of the Principal Components in TypeScript series, and it focuses on the application of PCA to a visual explanation of vision neural networks. Principal Components in TypeScript Previous parts: Part 1: https://hackernoon.com/principal-components-in-typescript-part-1 Part 2: https://hackernoon.com/principal-components-in-typescript-part-2-how-pca-actually-works-under-the-hood Part 1: https://hackernoon.com/principal-components-in-typescript-part-1 https://hackernoon.com/principal-components-in-typescript-part-1 Part 2: https://hackernoon.com/principal-components-in-typescript-part-2-how-pca-actually-works-under-the-hood https://hackernoon.com/principal-components-in-typescript-part-2-how-pca-actually-works-under-the-hood If you need a TL;DR, just go through the code or grab the package here on npm If you need a TL;DR, just go through the code or grab the package here on npm here on npm Not a Code Blog This is not a code blog. There’s no easy copy-paste solution here. If that’s what you want, go straight to the source code above. Now this post attempts to use PCA in a totally different direction compared to the vanilla dimensionality reduction or to data compression discussed in the earlier posts. Basically, for this post, I'm going to walk you through how to attempt to uncover insights from CNN features. But before that, we must answer a very important question. Are CNNs outdated? Is this the age of Transformers? Are CNNs outdated? Is this the age of Transformers? Not exactly, as ConvNext proved in 2022, CNNs still have a role to play in most vision tasks, as well as being pretty much the only option for highly performant edge deployment on extremely compute-limited devices. ConvNext Since we have now answered the above question unconvincingly, let us attempt to figure out how to do it. A note of warning, while I have specified Typescript here (since that is what my library is written in), we still do not have a convincing CNN solution in pure JS or Typescript. The good news, I am working on one, and even though it isn't close to release, I am going to leave the link to my
May 29, 2026 · via hackernoon.com
Figures Abstract In the process of urban kitchen waste sorting, robots often encounter issues such as slipping and empty grabs when attempting to grasp dirty waste objects like plastic bottles and glass bottles. This paper proposes a garbage grasping framework based on the Channel Exchange Generative Residual Inference Network (CE-GR-NET), which synthesizes optimal grasping trajectories through the fusion of hierarchical visual features. The object detection network identifies and locates recyclable bottles among solid waste, while CE-GR-NET uses RGB and depth images to generate grasping points for plastic recyclable bottles. Experimental results show that, on the Cornell Grasping Dataset, the proposed method achieves good inference performance and fast inference speed in single-object solid waste scenarios, ultimately generating grasping boxes for plastic recyclable bottles in RGB images. On the self-constructed multi-source urban kitchen waste images, the proposed method generates grasping boxes for targets and regresses the corresponding object categories simultaneously, achieving an image-based model accuracy of 96.03%, an object-based model accuracy of 94.40%, and a grasping object classification accuracy of 97.87%. Citation: Xiao X, Liu D, Qin H (2026) A grasp point generation algorithm for waste handling based on a generative reasoning network. PLoS One 21(5): e0349864. https://doi.org/10.1371/journal.pone.0349864 Editor: Marco Antonio Moreno-Armendariz, Instituto Politecnico Nacional, MEXICO Received: November 15, 2025; Accepted: May 6, 2026; Published: May 29, 2026 Copyright: © 2026 Xiao 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: All relevant data are within the paper. Funding: This work was supported in part by the Project of the Hunan Provincial Natural Science Foundation (2026JJ90097), the Scientific Research Fund of Hunan Provincial Education Department (25A0672), and the Research and Practice of the
May 29, 2026 · via journals.plos.org
CHARLOTTE — Court documents provide new details about a shooting inside a northwest Charlotte Walmart.
Channel 9 reported on Thursday that the Charlotte-Mecklenburg Police Department arrested 17-year-old Xavier Tirado for reportedly shooting another 17-year-old on May 21 at the Walmart on Callabridge Court.
The affidavit says surveillance video shows the person who was shot was reaching into his waistband and grabbing a gun. As this happened, shots were fired at the victim.
The shooting wasn’t captured on video, but CMPD says cameras caught Tirado and a second suspect fleeing the store.
CMPD says investigators used NCDMV facial recognition technology to identify Tirado as the suspect.
He was charged with attempted murder and held on a $25,000 bond. He posted that bond the next morning and was released from jail.
©2026 Cox Media Group
May 29, 2026 · via wsoctv.com
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May 29, 2026 · via facebook.com
UK selects Cognitec for facial age estimation in asylum assessments The UK government has selected a vendor for facial age estimation. The £322,000 ($433,745) contract begins on June 1, 2026 and runs for three years. The contract award notice lists established British IT provider Akhter Computers Limited as the main contractor, with biometrics services subcontracted to German facial recognition specialist Cognitec. The award moves facial age estimation from pilot and policy discussions into operational use within the UK’s asylum system. It also places the technology at the center of a contentious debate over migration, children’s rights and the role of AI in government decision-making. The contract comes as the Home Office formally incorporates facial age estimation into its asylum age-assessment framework. Newly published guidance explains how facial age estimation can be used to support immigration officers making initial decisions about whether undocumented arrivals should be treated as children or adults. The document outlines how facial age estimation can be used to classify asylum seekers. “Asylum seekers frequently arrive at the UK border with no official identity documents and so it can sometimes be difficult to know for sure which are children,” it says. “A child might look like an adult when they are not, and vice versa. Adult migrants sometimes claim to be children to prevent their removal and to take advantage of protections designed for minors. Sometimes children say they are adults to avoid being separated from friends. In some cases, a person simply doesn’t know their actual chronological age.” “If there is reason to doubt someone is the age they say they are, immigration officers must make an initial age decision to determine whether to treat them as a child or an adult. Facial age estimation, or FAE, can help the officials whose job it is to make
May 29, 2026 · via biometricupdate.com
Suspect arrested for North Las Vegas murder after facial recognition match LAS VEGAS (KSNV) — A suspect has been arrested in a North Las Vegas murder case after detectives got a facial recognition match, according to police and court records. Kenneth Rapier, 27, was booked into Clark County Detention Center on Wednesday, May 27, on suspicion of open murder, robbery and grand larceny of a motor vehicle. A North Las Vegas Police affidavit identifies Rapier as a suspect in the Feb. 1 shooting of Genaro Patterson Jr. near E. Centennial Parkway and 5th Street. The gunman then took a Chevrolet Corvette that had been rented and drove off. According to the affidavit, a witness to the shooting told officers that the victim was his cousin. When police asked him to identify the shooter, the witness only wrote down "Black male name 43." Detectives reviewed social media video and saw one video clip showing a man wearing a red hoodie looking at a Chevrolet Corvette. At the bottom of the clip was a tag that read "43" and "Kennape." Police took images from an Instagram account belonging to "Kennape" and ran it through a facial recognition program, which came back with a match to Rapier. Detectives compared the images and determined they were a match. Officers found the Corvette and obtained its GPS data. They tracked it to a steakhouse near the Las Vegas Strip hours before the shooting and reviewed its surveillance video. Police wrote that Rapier could be seen getting into the passenger seat of the car. Detectives then got a warrant for Rapier's cell phone data, which placed him at the steakhouse and the area of the shooting. Court records indicate Rapier is being held without bail. His next court appearance is scheduled for June 10.
May 29, 2026 · via news3lv.com
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May 29, 2026 · via youtube.com
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China unveils AI system to automate satellite targeting and surveillance
Researchers say their AI ‘brain plus tool army’ cuts analysis time and overcomes obstacles without human intervention
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Chao Kongin Beijing
The United States is reportedly bringing a powerful new weapon to the Iran war: large artificial intelligence (AI) models tasked with automating every stage of the targeting process, from satellite imagery analysis to final strike selection.
But how these systems operate remains strictly classified, with incidents such as the February bombing of a primary school that killed more than 200 children in southern Iran raising public concerns about AI’s potential role in committing war crimes.
Now China has taken a first step towards transparency.
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Chinese aerospace researchers last month unveiled the Air Target Agent System, a powerful LLM agent collaboration AI tool designed to take satellite surveillance beyond image recognition – enabling it to analyse what it sees, draw conclusions and act on them autonomously.
The system combines large language models (LLMs) with AI agents capable of breaking down complex tasks, automatically selecting algorithms, coordinating workflows and recovering from failures without human intervention.
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May 29, 2026 · via scmp.com
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) is a global Hologram Augmented Reality technology provider with a new deep convolutional neural network designed for image recognition. The company has developed a model utilizing quantum parameterized circuits as its core computing structure, a departure from traditional deep learning approaches and a step toward harnessing quantum computing’s parallel processing capabilities. This technology addresses limitations in computational complexity, memory consumption, and training efficiency by combining quantum circuits with a quantum-classical hybrid training mechanism. The model uses quantum parameterized circuits as its core computing structure, performs feature extraction on image data through quantum convolutional layers, and utilizes a quantum classification layer to complete the final recognition task. The quantum feature fusion module can reduce some computational overhead compared to traditional methods. Quantum Parameterized Circuits for Image Recognition WiMi Hologram Cloud Inc. has demonstrated a quantum deep convolutional neural network achieving progress, signaling a deepening of the application of quantum machine learning in artificial intelligence tasks such as image recognition, and also provides a new research direction for the realization of future large-scale quantum intelligent computing systems. WiMi’s approach centers on a fundamentally different computational structure, quantum parameterized circuits, as the core of its deep convolutional neural network. The new model utilizes these circuits to perform feature extraction on image data through quantum convolutional layers, culminating in a quantum classification layer for final recognition. This architecture mirrors the hierarchical structure of classical deep convolutional neural networks, but crucially, it leverages the parallel computing capability of quantum circuits to enhance processing speed when handling high-dimensional data. The system begins by mapping classical image data into the quantum state space via a data encoding module, a necessary step as quantum computers operate on quantum state information. This conversion of pixel information into qubit probability amplitudes is achieved through
May 29, 2026 · via quantumzeitgeist.com
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May 28, 2026 · via youtube.com
AI will be used to estimate age of asylum seekers from next year An Artificial Intelligence (AI) age estimation tool that aims to detect adult migrants posing as children will be deployed at the UK's borders next year. A software company has been awarded a contract to develop and test the technology, which will estimate a person's age by analysing photographs of them taken at the border. The Home Office says the technology will make it easier to identify adult migrants "attempting to game the system", after initial testing indicated "promising performance and accuracy". But Human Rights Watch urged the government to scrap the scheme, describing it as "unproven technology" that will undermine the protections vulnerable children are entitled to. Unaccompanied child migrants receive support from local councils and are housed in the care system rather than more traditional asylum accommodation such as hotels. They are entitled to legal protections which can simplify the asylum application system and make it easier to stay in the country for longer. The decision to use the software comes after years of heightened levels of people crossing the English Channel in small boats and claiming asylum at the border. A total of 111,084 people claimed asylum in the UK in the year ending June 2025, 14% more than in the previous year. In the year ending March 2026, more than 6,400 migrants claiming to be children were age assessed at the border, with 43% found to be adults, according to Home Office data. A report carried out by the UK government's independent immigration inspector last year found cases where adult migrants had been classified as children - and cases where child migrants had been wrongly classified as adults. The report said in the absence of a "foolproof" test, it was "inevitable that some age
May 28, 2026 · via bbc.com
Instagram post, facial recognition leads police to suspect in NLV slaying Authorities used social media and facial recognition technology to track down a suspect in the killing of a man in North Las Vegas, according to police and court records. Kenneth Lee Rapier, 27, is charged with murder, grand larceny of a motor vehicle and robbery with use of a deadly weapon in the Feb. 1 slaying of Genaro Patterson Jr., 36. Patterson suffered a gunshot wound to the chest in the 100 block of East Centennial Parkway, near Losee Road, at 3:06 a.m. Feb. 1, police said. A North Las Vegas police detective wrote in an affidavit that video surveillance from the area where the killing occurred showed a rented Corvette and the victim’s vehicle at the crime scene prior to the killing. One witness told police she heard people arguing prior to the shooting, and another witness told police that the gunman was known as “43,” police said. Police later located Instagram videos posted the day prior that police said depicted the same Corvette. At the bottom of the clip was the tag “43,” police said. A profile photograph from the Instagram account was put into facial recognition software, which returned a match for Rapier, police said. Police then located more surveillance video from a Las Vegas business that depicted Rapier as a passenger in the Corvette. Police said a warrant for Rapier’s cell phone produced data placing him in the area of the killing at the time it unfolded. Court records show an arrest warrant was issued for Rapier on Feb. 9 and he was arrested on May 11. His next court appearance in North Las Vegas Justice Court is scheduled for June 10. Contact Glenn Puit at gpuit@reviewjournal.com.
May 28, 2026 · via reviewjournal.com