Abstract Accurate detection of skin cancer detection using RGB images remains a challenge due to multiple factors including variability in lesion appearance, difference in skin types, and the clinical interpretability of the models. To address these challenges, we present a unified feature-fusion framework that integrates deep learning methods for accurate classification of dermoscopic skin lesions. Our proposed method consists of preprocessing in which we performed normalization, class aware selective augmentation, followed by feature-level fusion from three customized deep learning architectures i.e.; VGG-16 with adaptive layer configuration, ResNet-50 with dermatological feature enhancement, and a Vision Transformer (ViT) with dynamic patching. These extracted features are then fused to form a comprehensive feature vector and classification is done using an HDFNet which is a two-layer Deep Neural Network (DNN). We have trained and tested the proposed model on four publicly available datasets including ISIC 2019, ISIC 2020, PAD-UFES, and DermQuest DERMIS. We achieved classification accuracy of 94.5% and an AUC-ROC of 97%. Our proposed method outperforms the existing state-of-the-art models and also provide interpretable predictions supported by Grad-CAM-based visual explanations. Similar content being viewed by others Data availability The datasets generated and/or analyzed during this study are available in the ISIC Challenge repository at https://challenge.isic-archive.com/data/#2019, https://challenge.isic-archive.com/data/#2020, https://data.mendeley.com/datasets/zr7vgbcyr2/1, https://vip.uwaterloo.ca/skin-cancer-detection/. References Mukadam, S. B. & Patil, H. Y. Skin cancer classification framework using enhanced super resolution generative adversarial network and custom convolutional neural network. Appl Sci 13, 1210 (2023). Al-Rasheed, A., Ksibi, A., Ayadi, M., Alzahrani, A. I. & Mamun Elahi, M. An ensemble of transfer learning models for the prediction of skin lesions with conditional generative adversarial networks. Contrast Media Mol Imaging 2023, 1–15 (2023). Thanka, M. R. et al. A hybrid approach for melanoma classification using ensemble machine learning techniques with deep transfer learning. Comput Methods Progr Biomed Update 3, 100103 (2023). Kahia,
Apr 23, 2026 · via doi.org
Disneyland offers alternative to controversial tech change to theme park entry Disneyland is offering guests an alternative to a controversial tech feature that is now shaping the way guests enter the park. Now, guests who would rather not hand over a “faceprint” to stroll down Main Street U.S.A. have a choice. Disneyland and Disney California Adventure also include special entry lanes that skip facial recognition, according to the Orange County Register. The entry lets visitors who feel uneasy about sharing their biometric data or fret over cyber risks enter the parks without joining the system, which was originally meant to speed up entry and stop fraud. Both parks offer clearly marked lanes for those who “do not wish to participate” in the service. Disneyland staff may still snap photos of guests in these lanes, but the images skip any facial recognition processing. Kids can also use the opt-out lanes, as long as a parent or guardian gives the green light. Under the standard system, Disneyland photographs daily visitors and annual passholders at the entrance and converts those images into unique numerical values using biometric technology. Those values are then used to match a guest’s face to the photo tied to their ticket or pass. Disney says it deletes those biometric numerical values within 30 days, unless they need to be retained for legal or fraud-prevention reasons, and maintains that it has safeguards in place to protect visitor data. “The security, integrity, and confidentiality of your information are extremely important to us,” the company states, while acknowledging that “no security measures are perfect or impenetrable.” The shift comes as concerns grow about surveillance and tracking in public spaces, giving Disneyland guests a way to skip the tech altogether if they choose. Download The California Post App, follow us on social, and
Apr 23, 2026 · via nypost.com
Jewish man called 'baby killer' in antisemitic attack Warning: This article contains details that some readers may find distressing A Jewish man has said he feared for his life after being assaulted and called a "baby killer" in an antisemitic attack. The victim, who gave his name as Moshe, told BBC News he was approached while working at a property in Slough on Monday when a man began shouting abuse. Video footage filmed by Moshe shows an aggressive man threatening to break his jaw and attempting to knock a phone from his hand. Thames Valley Police said a 48-year-old man from Slough has been arrested on suspicion of racially or religiously aggravated assault and public order offences. Moshe said he was wearing a black kippa, a traditional head covering worn by Jewish men, at the time of the incident on Elliman Avenue. "I was thinking two things," he said. "Number one was survival. Number two, I was just berating myself: 'What were you thinking going out like this, in England, as a visible Jew?'" In the video, the man can also be seen repeatedly swearing, asking "what are you doing round here, bro?" and calling Moshe a "dirty Jew". He said the incident had left him feeling unsafe in some parts of the country. Police said officers were called at 16:36 BST and that a man in his 20s had been threatened. Det Insp Terry Dixon said the incident would "cause a great deal of concern" within Jewish communities. He added: "Antisemitic incidents, or indeed any hate-related incident, will not be tolerated and we will do everything in our power to bring those responsible to justice." Shomrim, a volunteer group that supports Jewish communities, described the incident as an "abhorrent, vile and unprovoked antisemitic assault".
Apr 23, 2026 · via bbc.com
Abstract Ancient cultural ruins, as tangible evidence of human-environment interaction, provide invaluable historical insights and critical resources for addressing global challenges like climate change and population growth. However, most ruins are indistinguishable and challenging to identify, as they have merged with the surrounding sediments through prolonged natural accumulation and anthropogenic activities. Using SNV in conjunction with the ResNet50 model, this study proposes a novel method for achieving high-precision classification of spectral data from ancient human ruins. The ResNet50 model achieves a classification accuracy of 94.86% when used independently, whereas the accuracy improves to 96.60% when the ResNet50 model is combined with SNV. The model exhibits exceptional classification performance, even when trained with a limited number of spectral image samples from ancient ruins. The superiority of the SNV + ResNet50 model provides a pioneering and effective method for the rapid and accurate identification of ancient human relics using visible-near-infrared spectroscopy. Similar content being viewed by others Introduction Since their emergence, human beings have continuously interacted with the natural environment, accumulating a rich legacy of cultural remains throughout this prolonged and dynamic process. These ruins encapsulate the history of human cognition, adaptation, utilization, and transformation of nature, acting as a repository of extensive information on environmental evolution and human-land interactions. Therefore, ancient ruins can provide invaluable historical insights and serve as crucial resources for addressing global challenges such as climate change and population growth, thereby promoting the sustainable development of human society1,2,3. Under the influence of prolonged natural accumulation and anthropogenic activities, most human ruins have gradually become integrated into the surrounding environment4,5,6. Consequently, differentiating these remains from natural sediments and delineating their spatial morphology and distribution poses a significant challenge. The traditional manual investigation is not only inefficient but also heavily reliant on subjective experience for interpretation7,8,9. It is imperative to
Apr 23, 2026 · via nature.com
ESP-Claw Lets You Build IoT Projects via Chat Using ESP-Claw, you can chat with your ESP32 board to bring hardware projects to life without writing a single line of code. Are you still spending hours writing firmware for your embedded systems projects? If you are using an ESP32 microcontroller, you might not need to anymore — at least in some cases. Espressif has just released ESP-Claw, a chat coding AI agent framework for IoT devices. You simply flash the ESP-Claw firmware to your development board and wire it to any additional hardware you need, then send a message to ESP-Claw via a messaging app like Telegram explaining how it should work. Within seconds, it just works. Users can define device behavior through natural language, with the system dynamically generating Lua scripts to control hardware like LED strips, displays, or motors. Once validated, those scripts can be stored locally, ensuring reliable operation even without a connection to the Large Language Models (LLMs) that created them. This hybrid model — LLM-generated logic combined with deterministic Lua execution — offers both flexibility and stability. ESP-Claw is built around an event-driven architecture. Rather than waiting for explicit user commands, devices can react proactively to real-world triggers such as sensor readings or system events. For time-sensitive applications, local rules execute in milliseconds, bypassing any need for cloud interaction. If no rule applies, the system can escalate to an AI model for deeper analysis or even offload complex tasks like image recognition. Unlike typical AI assistants that lose context between sessions, ESP-Claw organizes long-term data into categories such as user preferences, device events, and behavioral rules. These are stored locally with lightweight tagging, allowing the system to retrieve relevant context efficiently without heavy database overhead. Over time, the device can identify patterns and suggest new automations.
Apr 23, 2026 · via hackster.io
Panasonic adds QR-based biometric onboarding to streamline site access Panasonic Connect has introduced a new QR‑based face registration feature for its KPAS Cloud access management service. The new feature is aimed at streamlining biometric onboarding at factories, construction sites and other high‑traffic facilities. In 2025 the Japanese company launched the KPAS Cloud Site Management Service. It uses Panasonic’s facial recognition technology to control entry and exit for contractors, staff and visitors. The company says the new QR code feature is designed to remove one of the biggest operational bottlenecks as administrators previously had to collect face images in advance or register users on site. Under the updated workflow, administrators generate a QR code from the management portal and send it to the user by email. On arrival the user scans the QR code at a dedicated terminal and captures their facial image. They then complete registration and gain access to authorized areas. Panasonic says the QR codes contain restricted identification data that can only be interpreted in authorized environments. This prevents third‑party access and the mechanism is now under patent application, the company said in a post on its website (in Japanese and machine translated into English). The update is part of a broader plan to expand KPAS Cloud into a unified platform for on‑site operations, according to Panasonic. Future additions will include facial recognition‑based attendance management, door‑level access control and integration with security camera video feeds. Panasonic says the goal is to consolidate functions that are currently handled by separate systems and provide a more comprehensive digital identity and access management for industrial and logistics environments. From Pakistan to the U.S. and the Philippines, QR codes are linking up with identity verification. In the U.S., CMS is aiming to modernize patient data access with patients able to take a
Apr 23, 2026 · via biometricupdate.com
Macrodata Refinement Lets Users Classify Data Through Emotions Ellen Smith — April 23, 2026 — Tech References: teal-moxie-e7f0e6.netlify.app Macrodata Refinement Application is an interactive digital experience that combines data categorisation with abstract, emotion-based classification. Users engage with a retro-inspired interface to sort numerical data into predefined emotional categories such as Woe, Frolic, Dread, and Malice. The process blends analytical input with subjective interpretation, creating a hybrid task that is both structured and intuitive. Rather than serving a traditional data processing function, the application is designed to simulate or gamify decision-making and pattern recognition. It is typically positioned as an experiential or conceptual tool, appealing to users interested in immersive or narrative-driven interactions. The application reflects broader trends in interactive design where elements of gamification, nostalgia, and abstract systems are combined to create engagement. Its structure highlights how digital interfaces can be used to explore unconventional approaches to data interpretation. Image Credit: Macrodata Refinement The process blends analytical input with subjective interpretation, creating a hybrid task that is both structured and intuitive. Rather than serving a traditional data processing function, the application is designed to simulate or gamify decision-making and pattern recognition. It is typically positioned as an experiential or conceptual tool, appealing to users interested in immersive or narrative-driven interactions. The application reflects broader trends in interactive design where elements of gamification, nostalgia, and abstract systems are combined to create engagement. Its structure highlights how digital interfaces can be used to explore unconventional approaches to data interpretation. Image Credit: Macrodata Refinement Trend Themes - Emotion-based Data Classification — Translating numeric datasets into affective categories creates potential for models that surface sentiment-informed patterns and predictive signals not apparent in traditional metrics. - Gamified Data Interfaces — Interactive, game-like sorting experiences suggest novel engagement frameworks where user-driven play generates labeled datasets and
Apr 23, 2026 · via trendhunter.com
Three arrests from town centre facial recognition Three people were arrested after a police force used live facial recognition (LFR) vans in a town for a second time, figures show. Thames Valley Police (TVP) used LFR cameras in Slough's High Street on Tuesday and scanned 7,729 faces between about 11:00 BST and 12:45. The technology has been criticised by some civil liberty groups but a challenge against the Metropolitan Police's use of it was dismissed at the High Court the same day. Following the case's conclusion, policing minister Sarah Jones said law-abiding citizens have "nothing to fear" as the technology "only locates specifically wanted people". Youth worker Shaun Thompson and Silkie Carlo, director of campaign group Big Brother Watch, brought the case because of concerns that LFR could be used arbitrarily or in a discriminatory way. Thompson was misidentified as his brother by LFR in 2024 and stopped, detained and questioned by police in London after he was matched by the technology. At the time his brother was on bail for a suspected violent offence. TVP's figures show about 52,600 faces were scanned at the Eden Centre in High Wycombe on 3 March. Though two people were arrested, at least one was not connected to LFR's deployment. It was also used at The Lexicon in Bracknell on 5 March, where just over 20,000 people's faces were scanned. Another three people were arrested after TVP's vans were deployed to Cornmarket Street in Oxford on 20 March. About 37,700 people's faces were scanned there. They were also used in Market Square in Aylesbury on 13 April and in Chesham's High Street on 20 April. About 12,100 and 6,400 people's faces were scanned there, respectively but no one was arrested.
Apr 23, 2026 · via bbc.com
Abstract Head computed tomography (CT) imaging is a widely used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull and cerebrovascular system. It is commonly used as the first-line imaging in neurologic emergencies given its rapidity of image acquisition, safety, cost and ubiquity. Deep learning models may facilitate detection of a wide range of diseases. However, the scarcity of high-quality labels and annotations, particularly among less common conditions, substantially hinders the development of powerful models. To address this challenge, we introduce FM-HCT, a Foundation Model for Head CT for generalizable disease detection, trained using self-supervised learning. Our approach pretrains a deep learning model on a large, diverse dataset of 361,663 non-contrast 3D head CT scans without the need for manual annotations, enabling the model to learn robust, generalizable features. Our results demonstrate that the self-supervised foundation model substantially improves performance on downstream diagnostic tasks compared to models trained from scratch and previous 3D CT foundation models trained on scarce annotated datasets. Similar content being viewed by others Main Head computed tomography (CT) is widely used for rapid evaluation of neurological emergencies such as trauma, haemorrhage and stroke. Although CT is faster, more accessible and less expensive than magnetic resonance imaging (MRI), it provides lower soft-tissue contrast, limiting sensitivity for many neurological conditions. Improving diagnostic capability from CT therefore provides substantial clinical value. Artificial intelligence (AI) has the potential to enhance CT interpretation and support clinical decision-making by enabling earlier and more accurate diagnosis. However, progress in AI-based head CT analysis remains limited by both data availability and model design. Public datasets such as RSNA1 and CQ500 (refs. 2,3) are relatively small and primarily focus on haemorrhage detection, restricting broader clinical applicability. In addition, many existing approaches rely on two-dimensional (2D) convolutional networks that process
Apr 23, 2026 · via nature.com
The European Union’s new Entry/Exit System (EES), launched with a phased rollout in October 2025, will fundamentally transform how non-EU travellers move across borders in the 29-country Schengen Area. One key requirement is the capture of biometric data – facial images and fingerprints – along with passport details and travel history. While all Schengen border points must have EES fully deployed by April 2026, the phased rollout has highlighted both the promise and operational challenges of large-scale biometric implementation. Yes, EU residents and visitors can say goodbye to smudgy passport stamps and hello to a fully digital travel experience, but this also underscores the critical element of accuracy, resilience and scalability in these systems. This wake-up call has upended staid passenger processing systems that include manual identity checks and document-based verification. The implementation of these new systems provides real-world data on their effectiveness, and the early signs are beyond encouraging. Biometric verification can reduce airport processing times by 30% to over 80% compared to manual checks, depending on implementation. U.S. Customs and Border Protection reports that biometric Global Entry kiosks cut processing time from 45 seconds to under six seconds per traveller. EES is more than a compliance mandate – it’s a strategic shift toward smarter, more secure borders. For airports, it’s an opportunity to modernise operations, improve passenger flow and align with the EU’s broader digital transformation goals. By 2026, more than half of airports expect to use biometrics at check‑in and bag drop, and around 70% of airlines plan to have biometric identity management systems in place, reflecting both the appeal of dependable AI‑enabled processes and the growing risks of clinging to legacy workflows. Passengers have also expressed an overwhelming preference for biometric experiences. Roughly three-quarters of travellers worldwide say they would share their biometric information if it
Apr 23, 2026 · via regionalgateway.net
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Apr 23, 2026 · via openpr.com
Face-selective neurons in the macaque visual cortex dynamically change their tuning properties, according to a study published last month in Nature. The findings suggest that artificial neural networks—which are stably tuned—may not fully model visual systems in primates. Neurons in the inferotemporal cortex initially respond to both faces and inanimate objects, the study found. But then those cells switch to respond to specific facial features, including inter-eye distance and hair color. That switch likely corresponds to a shift from the brain recognizing the presence of a face to gauging what that face looks like, according to the study investigators. “[This shift] is a phenomenon that has never been characterized before,” says Laura Gwilliams, assistant professor of psychology at Stanford University, who was not involved in the work. “We need to rethink how neurons code information.” Conflicting evidence has fueled a debate in face perception research: On the one hand, the visual cortex contains clusters of highly selective neurons—known as face patches—indicating that the region uses specialized mechanisms for face processing. And stimulating face-selective brain areas in primates, including humans, distorts the perception of faces but not of non-face objects. Yet, other studies suggest that the neurons employ a general code to respond to a range of visual features. The new paper “offers a potential solution to solve this controversy,” says Shahab Bakhtiari, assistant professor of psychology at the Université de Montréal, who did not take part in the study. “The two theories were looking at the same thing but at different [timepoints].” Neurons in the inferotemporal cortex initially use a general code, then switch to a face-specific code in a matter of milliseconds, the new study found. By contrast, convolutional neural networks—image-processing algorithms that attempt to model visual pathways—use a nonspecific approach for facial recognition. “I think it’s very interesting
Apr 23, 2026 · via thetransmitter.org
To evaluate SSD performance in real-world AI workloads, we tested the load times of various deep learning models. These include both image classifiers (such as ResNet, VGG19, and EfficientNet) and a large language model (LLaMA 2 7B). Since model sizes and complexities vary widely, we calculated the geometric mean across all tests to provide a clear and balanced comparison. The chart below gives an at-a-glance view of which drives handle AI-related loading tasks most efficiently. The chart above presents the average of all our comparisons, including tests with LLMs (Large Language Models) such as LLaMA 2 7B, as well as various image classification models that will be detailed below. Individual Test Results Large Language Models To evaluate real-world AI performance, we use a benchmark that measures the time it takes to load the LLaMA 2 7B large language model from SSD storage into system and GPU memory. The procedure replicates a typical machine learning workflow, where models need to be initialized quickly for inference or fine-tuning. Using the Hugging Face Transformers library in offline mode ensures the model is loaded entirely from local storage, without network interference. The tokenizer and full model are preloaded using PyTorch in float16 precision to simulate a realistic deployment scenario, and the total loading time—from disk to memory—is recorded. By running the benchmark test across multiple SSDs, we can identify which ones deliver the fastest model initialization times, a critical factor for AI tasks that demand low startup latency. Image Classification Models Benchmark Our benchmark measures the loading time of large image classification models by analyzing two stages: transfer from SSD to system memory, and then to the GPU. Each model is loaded 20 times to calculate an average and standard deviation for reliable results. The goal is to compare loading efficiency across architectures—particularly important
Apr 23, 2026 · via techpowerup.com
It’s a phrase we use a lot in our community, “Drink the Kool-Aid”, meaning becoming unreasonably infatuated with a dubious idea, technology, or company. It has its origins in 1960s psychedelia, but given that it’s popularly associated with the mass suicide of the followers of Jim Jones in Guyana, perhaps we should find something else. In the sense we use it though, it has been flowing liberally of late with respect to AI, and the hype surrounding it. This series has attempted to peer behind that hype, first by examining the motives behind all that metaphorical Kool-Aid drinking, and then by demonstrating a simple example where the technology does something useful that’s hard to do another way. In that last piece we touched upon perhaps the thing that Hackaday readers should find most interesting, we saw the LLM’s possibility as a universal API for useful functions. It’s Not What An LLM Can Make, It’s What It Can Do When we program, we use functions all the time. In most programming languages they are built into the language or they can be user-defined. They encapsulate a piece of code that does something, so it can be repeatedly called. Life without them on an 8-bit microcomputer was painful, with many GOTO statements required to make something similar happen. It’s no accident then that when looking at an LLM as a sentiment analysis tool in the previous article I used a function GetSentimentAnalysis(subject,text) to describe what I wanted to do. The LLM’s processing capacity was a good fit to my task in hand, so I used it as the engine behind my function, taking a piece of text and a subject, and returning an integer representing sentiment. The word “do” encapsulates the point of this article, that maybe the hype has got it
Apr 22, 2026 · via hackaday.com
Face ID Vs Fingerprint: Which Is Better For Your Phone Security? Phone security has evolved well past requiring PIN numbers and passcodes to unlock them. These days, phones have biometrics, a form of identification that uses a person's unique traits to grant them access to the device. Two common forms of biometrics used in smartphones are Face ID (also known as facial recognition) and fingerprint scanning. No two faces or fingerprints are exactly alike, making them highly secure ways of preventing unwanted access to your phone. When it comes to Face ID vs. fingerprint for phone security, it can be hard to pick between the two. Since both have unique strengths and weaknesses, the better choice depends on several factors. But ultimately Face ID is more secure. However, it's best to ensure the phone uses 3D Face ID technology like you'll find on the value-packed Apple iPhone 17 or Huawei Mate 80 Pro. Android phones, like the Samsung Galaxy S26 Ultra, with all its cool features, use 2D facial recognition. This is less secure because it relies on flat image patterns instead of depth, which can be tricked by a photo. Still, fingerprints may be preferred if reliability and privacy are your biggest security concerns. Face ID has stronger security than fingerprints Security is the biggest reason to choose facial recognition over fingerprints for unlocking your phone. Basically, a device with this biometric feature maps your face by projecting thousands of invisible dots onto it, and then analyzing them to create a detailed 3D map. It's extremely hard to fool with a flat visual of yourself (photo or video) or a realistic mask. For instance, the TrueDepth camera on the iPhone will reject these tricks when it fails to identify the contours of a human face. Fingerprint scanners, on the
Apr 22, 2026 · via bgr.com
The American Civil Liberties Union has filed complaints against police departments in Montgomery, Prince George’s and Anne Arundel counties, claiming a false identification resulting from a facial recognition technology search led an innocent woman to be jailed for six months. Kimberlee Williams, an Oklahoma resident, said she had never been to Maryland before she was arrested on an outstanding warrant from Montgomery County while trying to drop off a food delivery order at a military base. “I was flown there in handcuffs, for a crime I had nothing to do with,” Williams said in an ACLU news release. “My family and I can’t get that time back, but I hope my experience will be a warning to police in Maryland and across the country that this technology can ruin lives. No family deserves to go through that.” The ACLU said Williams is the 14th person it knows of who was wrongfully arrested because of faulty facial recognition technology. Williams’ ordeal began when an investigator working for a bank uploaded an image of a suspect who had withdrawn thousands of dollars by impersonating account holders in Maryland branches. The investigator uploaded the image to a national LISTSERV of police and private investigators called CrimeDex. Someone on the LISTSERV ran the image through facial recognition and sent back Williams’ name and photo as a match. Read More The bank investigator notified a Montgomery County Police Department detective of the match in a memo, citing “facial recognition software,” and the department obtained an arrest warrant “without any independent investigation,” according to the ACLU. But that detective in Montgomery County never disclosed how he had come across Williams’ name, according to the ACLU. Instead, he “misleadingly claimed that Ms. Williams had been ‘identified’ as the suspect and that the detective had confirmed the identification
Apr 22, 2026 · via thebanner.com
Air Force Gen. Steven Nordhaus, chief, National Guard Bureau, presents the 2026 Senator Ted Stevens Leadership Award during the Tragedy Assistance Program for Survivors Gala in Washington, D.C., Mar. 17, 2026. The award was presented to Sara Wilson in recognition of her advocacy and support for Gold Star Families. (U.S. Army National Guard photo by Staff Sgt. Kelly Boyer)
| Date Taken: | 03.17.2026 |
| Date Posted: | 04.22.2026 15:25 |
| Photo ID: | 9633491 |
| VIRIN: | 260317-A-KB362-1002 |
| Resolution: | 5875x3909 |
| Size: | 2.23 MB |
| Location: | DISTRICT OF COLUMBIA, US |
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This work, Nordhaus Presents TAPS Award [Image 3 of 3], by SSG Kelly Boyer, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Apr 22, 2026 · via dvidshub.net
A transgender fan of the New York Knicks basketball team was tracked over a two-year period using sophisticated face recognition technology at Madison Square Garden (MSG), according to a lawsuit by a fired former security official who worked there. The woman was later banned from the venue. The suit alleges that Nina Richards (not her real name) became a fixation for the head of security working for James Dolan, the owner of MSG, Radio City Music Hall, and the Sphere, among other venues that use the same surveillance technology. Dolan, scion of former cable TV titan Charles Dolan, also owns the Knicks and the New York Rangers hockey team. Related The lawsuit’s allegation follows news in 2024 that Dolan added the faces of up to 1,500 lawyers involved in litigation against his companies to his facial recognition system at MSG, banning them from the property. Security head John Eversole became aware of Richards in 2021, soon after games resumed at the Garden following the COVID shutdowns, Wired reports. Never Miss a Beat Subscribe to our newsletter to stay ahead of the latest LGBTQ+ political news and insights. Described by another security staffer as “a very large transgender woman, being a fan,” Eversole determined that Richards was a threat to players and the team’s image. He ordered her to be added MSG’s facial-recognition database and instructed staffers to perform an open source “work-up” on her, based on the images captured by their cameras and other intelligence gathered by security. Eversole allegedly wanted to keep Richards “away from the players.” Richards was targeted “because of her gender identity,” according to the suit filed by former MSG security staffer Donnie Ingrasselino. Employees who were forced to conduct the surveillance were often uncomfortable, according to another former staffer, since they believed it to be
Apr 22, 2026 · via lgbtqnation.com
Getty Images/iStockphoto/Urupong Future mission success requires overcoming today’s data sharing challenges Commentary Read more
Apr 22, 2026 · via federalnewsnetwork.com
Immigration and Customs Enforcement bureaucrats are reportedly planning to use specialty facial recognition glasses to collect data on Americans in real time, independent journalist Ken Klippenstein revealed. Financial statements viewed by Klippenstein point to the development of a facial recognition platform modeled after commercially available AI smart glasses, like Meta’s widely-panned “pervert glasses.” ICE’s in-house model, it seems, will allow agents to monitor video and reference vast federal databases of biometric information on subjects regardless of if they’ve been arrested, or even charged with a crime. “The project will deliver innovative hardware, such as operational prototypes of smart glasses, to equip agents with real-time access to information and biometric identification capabilities in the field,” read an ICE budget document leaked to Klippenstein. Perhaps most alarmingly, Department of Homeland Security insiders told the investigative journalist that the technology involved isn’t limited to immigration enforcement. “It might be portrayed as seeking to identify illegal aliens on the streets,” one anonymous DHS attorney told Klippenstein, “but the reality is that a push in this direction affects all Americans, particularly protestors.” That reveal comes just a few months after an incident in Maine in which an ICE agent admitted to scanning protestors’ faces with his phone. “We have a nice little database, and now you’re considered domestic terrorists,” the agent tells a couple who were out documenting the immigration agents in their community. In October, 404 Media reported that ICE agents were scanning peoples’ faces in order to check whether they were citizens. These targets for surveillance are often chosen at random — we now know that many of ICE’s arrests over the past year have been circumstantial, a far cry from the targeted enforcement of known criminals the Trump administration promised. Taken together, what began as surveillance infrastructure marketed for catching illegal immigrants
Apr 22, 2026 · via futurism.com