Can Ai Detect Skin Cancer? Exploring The Accuracy, Technology, And Future Of Ai Driven ...
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Image Recognition
Live <b>Facial Recognition</b> cameras have been deployed in Walton.
A dedicated van and officers were there this week in an effort to target shoplifting, anti-social behaviour and drug-related crime. LFR captures real-time video feeds of people’s faces, comparing them instantly against a pre-determined police watchlist to generate alerts. The method is controversial, but police say anyone not on its watchlist has their image deleted straight away.
Best <b>Facial Recognition</b> AI Tools in 2026
The best facial recognition AI tool depends entirely on what you are trying to do. A journalist verifying a source’s identity needs a different tool than a developer building KYC into a fintech app, and both need different tools than a security team deploying access control across a 50-camera facility. The global facial recognition market is projected to reach $14.55 billion by 2031, growing at 16.79% annually. But most “best facial recognition tools” articles dump consumer search engines, developer APIs, and enterprise platforms into one undifferentiated list, which helps nobody make a decision. This article splits the category into three sections matching the three buyer profiles: consumer face search tools (for finding where faces appear online), developer APIs (for building facial recognition into your product), and enterprise security platforms (for physical access control and surveillance). Pick the section that matches your use case. Part 1: Consumer AI Face Search Tools These tools let individuals upload a photo and find where that face appears across the internet. The primary users are people verifying online identities, checking dating profiles, monitoring their own image, and conducting OSINT investigations. 1. FaceCheck ID: Best for Scam Detection and Social Media Search FaceCheck ID searches social media profiles, dating apps, mugshot databases, sex offender registries, and scam report sites. Its defining feature is the safety layer: when a match appears on a known scam profile or offender registry, the tool flags it with a red warning icon (Scam Alert, Sex Offender, Many Accounts) before you click through. FaceCheck ID handles low-quality images (blurry screenshots, side profiles, cropped photos) better than any consumer face search tool I have tested. I ran the same blurry screenshot from a WhatsApp video call through both FaceCheck ID and PimEyes. FaceCheck ID returned 4 usable matches within 8 seconds. PimEyes rejected
Veridas, Fourthline merge to create full-stack identity platform
Why Veridas and Fourthline are building a new kind of identity platform Consolidation in the biometrics industry continues, as firms find synergies in technology, regulatory coverage, and customer base. Recently, Veridas announced a merger with Fourthline. Eduardo Azanza, CEO of Veridas, says the two companies complement one another in terms of geography, the type of customers they’re courting, and their product portfolio. “Some people, they might say, ‘they do more of the same’,” Azanza says, on the latest episode of the Biometric Update Podcast. “That’s not the case. As long as they’re delivering their systems for NEOs, NEO banks, NEO cryptos, etc., they’ve deployed a full end-to-end platform to do the KYC. As long as we’re working with more traditional banks, telcos, etc., we’ll be more vertical on producing biometric engines, anti-fraud capabilities, also the wallet capability that complements nicely with their KYC, payment sanction, anti-money laundering services and bank account verification.” “So, let’s say that the overlap in terms of technologies is minimal.” Azanza says identity remains fairly fragmented, but large clients increasingly want hassle-free products that cover the gamut of services – and divergent regulatory regimes demand flexibility. “The idea is that you can orchestrate with the very same components very complex, different journeys for different regulations,” he says. But a critical piece is owning the technology: “if you do not own the tech, as long as the challenges evolve on a monthly basis, you cannot keep up the pace.” For more on identity verification, the evolution of fraud and some novel insights on why digital ID is an environmentally friendly choice, check out the full episode. Listen to this episode at: Spotify, Apple, YouTube, Podbean Runtime: 00:26:32 Article Topics acquisitions | Biometric Update Podcast | biometrics | digital identity | Fourthline | Veridas Comments
5 Apps That Let Your Phone's Camera Identify Almost Anything
5 Apps That Let Your Phone's Camera Identify Almost Anything Gone are the days when smartphones used to be more about themselves and less about the cameras. Today's phones are built around cameras and not the other way around. Your phone's camera is no longer just a way to take photos and videos; it's basically a pocket encyclopedia that can tell you about anything you point it at. Be it a mystery plant that you spotted on the road or a pair of sneakers that you found your friend wearing. This shift did not happen overnight. It's the result of years of AI and image-recognition work quietly baked into apps most people already have installed. Your smartphone's camera has turned into a translator, a plant guide, a personal shopper, and even a stargazing companion. The best part is that for most tasks, you don't need to install any third-party apps. You can use Google Translate or iPhone's built-in translator to convert text from one language to another and have it read aloud. No matter what app you're using, it has the same underlying idea — your camera is doing the identifying so you don't have to guess or do a web search manually. We have compiled a list of the best apps that can let you learn about or understand things just by pointing your camera at them. Each one lets you learn about different categories like plants, fashion, home decor, and general objects just by pointing your camera at them, so you're never stuck wondering. Google Lens Google Lens is the closest thing you can get to an app that identifies anything your camera can see and tells you about it. When you start using Google Lens, you'll understand (and might appreciate) how far technology has advanced. Built into
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Google Deepmind unveils Gemini Robotics 2 to power robots of all shapes from tabletop ...
Google Deepmind unveils Gemini Robotics 2 to power robots of all shapes from tabletop arms to humanoids Google Deepmind has introduced Gemini Robotics 2, which it calls its most advanced vision-language-action (VLA) model yet. VLA models combine image recognition, language processing, and action control to help robots operate in physical environments. Deepmind says the model can control systems ranging from tabletop arms to full-body humanoid robots. The company describes Gemini Robotics 2 as an "intelligence layer" for a new generation of adaptive robots. It can manage full-body movement, perform fine motor tasks, and coordinate multiple robots, according to Deepmind. Developers can apply for early access through the waitlist. Google Deepmind also introduced Gemini Robotics ER 2, a model designed for "embodied reasoning." The term refers to understanding the physical world and deciding which actions to take based on that information. ER 2 acts as a higher-level control system for robots and replaces Gemini Robotics ER 1.6, released in April. The new model is available in Google AI Studio. AI News Without the Hype – Curated by Humans Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section. Subscribe now
If You Can Solve This Color Puzzle In Less Than 3 Minutes, You Have Perfect Color Vision
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! Have a great weekend — Huedoku #79 drops Monday! 🌈 🌈 New Huedoku drops every weekday at 4:00 a.m. PT / 7:00 a.m. ET. Comments
Italy's new <b>facial recognition</b> policy sparks EU law debate | brief
Italy's proposed policy to store facial biometric data for seven days from individuals captured on video in sensitive areas is facing scrutiny over its compliance with European Union laws, as reported by Biometric Update.The Italian Senate's EU Policies Committee approved a decree allowing the storage of facial biometric data from cameras in sensitive public areas for seven days. This move has drawn criticism from the data protection authority, Autorità Garante per la Protezione dei Dati Personali (GPDP), which deems the policy inconsistent with the EU's AI Act restrictions on live facial recognition. The GPDP argues that continuous biometric data collection and retrospective processing for investigations could be interpreted as live facial recognition, which is generally prohibited under the AI Act, except for specific exceptions like searches for missing persons, imminent terrorist threats, or identification of serious offenses.A representative for Premier Giorgia Meloni's office stated that Italy remains compliant with European regulations, including the AI Act. The debate intensified following recent public order incidents, including riots and violent protests. The European Commission has emphasized that facial recognition in publicly accessible spaces is prohibited but noted that a definitive assessment of the Italian policy requires a detailed review.Source: Biometric Update Get daily email updates SC Media's daily must-read of the most current and pressing daily news You can skip this ad in 5 seconds
Delhi Police Uses <b>Facial Recognition</b> At Jantar Mantar | Sparks Debate Over Criminal Record Claims
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Surveillance with AI: What the controversial new decree says
About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket © 2026 Google LLC
Did a OneDrive Photos app just appear on your PC? Here's what it does (and how to get rid of it)
Did a OneDrive Photos app just appear on your PC? Here's what it does (and how to get rid of it) Follow ZDNET: Add us as a preferred source on Google. ZDNET's key takeaways - Microsoft has pushed out a new Windows 11 app called OneDrive Photos. - The app lets you access, view, and edit your OneDrive photos. - The app groups photos of the same people via facial recognition. Earlier this week, I noticed a new app called OneDrive Photos pop up in the Start menu on my Windows 11 PC. Being the curious type, I opened the app to snoop around and see what it could do. As its name indicates, it lets you view and even edit any photos you've stored in OneDrive, either on your PC or in the cloud. But why did Microsoft push it out so quietly, and are there any downsides to using it? First, let's address at least one question. Also: My new favorite Windows app made my PC safer and more reliable - and it's free "OneDrive Photos is already generally available today, and we're now previewing its new gallery as 'Photos (Preview)' for Windows Insiders in the Experimental channel," Microsoft said in a statement shared with PC World. "This update arrived through a OneDrive update rather than a separate install." OK, that explains how it rolled out, but it raises another question. Microsoft says that the app is a preview for Windows Insiders in the Experimental channel. But I received it on my regular production Windows 11 PC, not on an insider build. And from what I've read, many other people did as well. Everything you can do with the OneDrive Photos app With that mystery afoot, let's check out the app to see just what it does. If
Chico Unified Board Member speaks about new cameras being installed at CUSD campuses
CHICO, Calif. - The Chico Unified School District's decision to install cameras with AI and facial recognition software has sparked community pushback since the school board approved them last December. Action News Now on Thursday met with Chico Unified School Board Member Tom Lando to get answers on data storage and who will have access to the new system. Community concerns centered on the AI facial recognition software and how children's data and likenesses could be stored and used. Many parents questioned why such a significant change was handled without public debate. In December of 2025, the CUSD board approved replacing the district's 12-year-old camera system on its consent calendar, where items are typically approved without debate. The decision involved not just the $1.9 million dollar price tag, but a complete shift from old CCTV cameras to AI-powered ones. Lando said the board didn't know the new cameras came with AI software capabilities when they approved the purchase. He said replacing district equipment, not just cameras, is routinely placed on the consent calendar. The district's IT department never told the board this wasn't a standard camera upgrade. It was a completely different system. "What happened was that in the process of replacing old equipment, just like we do every year. We replaced Chromebook, old devices. Our cameras were end of life and IT wanted to replace them. They found what they thought was the best way to do that. And they put it on our consent calendar with all our regular monthly and annual spending," Lando said. "From our point of view we were just replacing old technology with new technology as we often do." Lando pointed to a February meeting that hasn't gotten enough attention. In that meeting, the board voted 5 to 0 to prohibit the cameras from
<b>Facial recognition</b> devices spotted at Punggol Coast MRT, trial draws curiosity and privacy concerns
Facial recognition devices spotted at Punggol Coast MRT, trial draws curiosity and privacy concerns Facial recognition devices set up at Punggol Coast MRT station have caught the attention of commuters, while others have raised privacy concerns. When Stomp visited the MRT station on the morning of July 31, two facial recognition devices were mounted on separate priority gantries. A sign on the ground marked out a facial recognition detection zone. The device, roughly the size of a small tablet, had a camera mounted at the top with an on-screen arrow instructing commuters to “look at the camera”. According to a TikTok post by 8 Days, a station staff member said that the device is currently being trialled. Commuters would have to register to link their transport card with their facial recognition profile. In 2016, The Straits Times reported that Singapore Technologies (ST) Electronics had developed a facial recognition system — the Advance Fare Gate system — that can identify commuters as they pass through fare gates. Although local and overseas train operators have reportedly shown interest in the system, it is unclear if it is the same system being trialled at Punggol Coast MRT. A form of ‘surveillance’?: Commuters raise concerns Most commuters Stomp spoke to said they did not notice the device. Ms Lee, a 63-year-old retiree who was meeting her friends nearby, said she struggled to understand how commuters were meant to use the device. Her 66-year-old travel companion agreed, likening it to the Immigration and Checkpoints Authority’s (ICA) facial recognition system deployed at immigration clearance. Another commuter, 59-year-old Mr Chua, wondered if the device would make entering the MRT a hassle, especially for elderly commuters. “I don’t really know this technology stuff,” he added. On TikTok, some netizens viewed the technology with scepticism, asking if it was
A computer vision-based approach for automatically extracting data from bar chart raster ...
Figures Abstract Although bar charts are widely used in scientific publications, their rasterized format within Portable Document Format (PDF) files complicates automated data extraction, hindering large-scale evidence synthesis and meta-analysis. To address this, we developed and evaluated an automated pipeline for extracting quantitative data from bar charts embedded in the biomedical literature. The four-stage pipeline comprises (1) image extraction and panel segmentation, (2) optical character recognition (OCR)-based text detection, (3) image disassembly to identify chart components, and (4) data reconstruction using numeric parsing and axis-based interpolation. The system combines edge detection, morphological operations, and convolutional neural network (CNN)-based figure classification using a transfer-learned Inception v3 model. Performance was validated on randomized controlled trials in age-related macular degeneration, with manually annotated values from a semi-automated labeling tool as the reference standard, and agreement was assessed using Bland–Altman analysis. Across 28 bar charts from ten publications, the pipeline correctly recognized 92.9% (95% confidence interval [CI], 77.4–98.0) of figure types, 96.0% (95% CI, 94.2–97.3) of text blocks, and 79.1% (95% CI, 74.7–83.0) of bars. For numerical reconstruction, 81.2% (95% CI, 76.3–85.2) of bar values fell within ±5% of the reference standard, 63.0% (95% CI, 57.3–68.3) within ±2%, and 48.6% (95% CI, 43.0–54.3) within ±1%. Bland–Altman analysis showed a small negative bias of −0.18 (95% CI, −0.34 to −0.02), with 94.9% of differences within the limits of agreement. Most outliers arose from OCR digit misclassification or ambiguous bar boundaries. This proof-of-concept study demonstrates the feasibility of automated data extraction from bar charts using a hybrid approach that combines image-processing heuristics with CNN-based classification. Although currently limited to bar charts and a single clinical domain, the pipeline represents a step toward scalable, end-to-end systems for automated evidence extraction to support meta-analyses across the biomedical literature. Citation: Cardaras A, Kim S, Yuan Y, Livnat I, Yanagihara
Making AI Work for Your Greenhouse Business
Making AI Work for Your Greenhouse Business For growers looking to reclaim more time, reduce risk, and make better business decisions, artificial intelligence (AI) represents a meaningful next step that builds on the automation investments already in place. If you’ve already automated climate controls and environmental monitoring systems, for example, AI can then analyze that data and tell you something you didn’t already know: that a temperature pattern from last week is likely to affect your crop yield next month, or that a pest issue is developing before visible damage appears. “We’re all in the early stages of what AI can do,” says Mary Beth Cowardin, Vice President of Marketing and Member Engagement at AmericanHort. “Like anything new, you just have to try it and learn where it can be most valuable to grow and protect your business.” Start Small, Then Scale When people think about AI, they often picture sophisticated robotics or autonomous greenhouses. Cowardin encourages growers to first start by thinking smaller while being ready to grow your AI usage into ways that can have a larger impact. “AI is meant to help people do their jobs more efficiently and effectively,” she says. “It’s not meant to replace humans. It may change how certain tasks are completed, but the goal is to help people work faster and smarter.” Some of the most practical starting points involve administrative work that takes employees away from higher-value customer service and production. AI also can: - Write product descriptions - Draft social media posts - Prepare customer estimates - Assist with scheduling crews more efficiently When AI does these tasks, it frees up employees to focus on serving customers, growing, and managing the business. At Cultivate26, Rob Sproule of Salisbury Greenhouse shared insight into his evolution working with AI. In 2025, he
Retail: Kiehl's Expands Permanent Smart Mirror Rollout Across Five Markets
Kiehl's is installing interactive smart mirrors in stores across Europe, the Middle East and Asia as part of a long-term retail technology strategy rather than a temporary marketing campaign. Retail: Kiehl’s Expands Permanent Smart Mirror Rollout Across Five Markets Kiehl’s is expanding its use of interactive smart mirrors with permanent installations in stores across the UK, UAE, Singapore, Malaysia and China, extending a retail technology program that began with an augmented reality storefront in New York. Developed with spatial technology company Loook.AI, the mirrors use motion sensing and augmented reality to attract shoppers as they approach. The displays activate automatically and include facial recognition-style scanning to personalize the on-screen experience. According to the companies, the New York installation increased store foot traffic by 20%, prompting the retailer to expand the concept internationally. Permanent retail infrastructure Unlike many interactive retail displays that are used for short-term campaigns, the Kiehl’s installations will remain in stores as permanent fixtures, with additional markets expected over time. Dmytro Kornilov, CEO of Ffface.me, said interactive displays keep shoppers engaged longer than conventional digital signage. “Our recent A/B testing found shoppers typically watched standard video content for about two seconds, while interactive mirror experiences averaged between seven and 10 seconds,” he said. Growing use of interactive retail Retailers are moving toward using interactive digital experiences to keep customers engaged inside physical stores, and Smart mirrors have been used for virtual product demonstrations, personalized recommendations, and other in-store experiences as retailers blend digital technology with brick-and-mortar shopping.
Iowa law puts labeling, AI reminder requirements on conversational chatbots
Iowa law puts labeling, AI reminder requirements on conversational chatbots New legislation in Iowa puts guidelines on AI chatbots. Senate File 2417 passed unanimously in the House and Senate, meaning chatbots like Claude and ChatGPT must disclose to young users that they are not human, and refrain from encouraging kids to extend conversations. Moreover, a labeling requirement says chatbots must clearly label AI content and distinguish between things created by humans and generative models. As quoted in the Des Moines Register, Republican state Senator Kara Warme says “the main goal of SF 2417 is to make chatbots safer for users, especially children, by making them more transparent and less deceptive.” “We see a lot of value in human connection, and so we want to make sure it’s clear whether you are interacting with a human or something that’s technology or computer based.” The regulation is not extraordinary – many jurisdictions are considering restrictions on chatbots, as pressure mounts from parents and civil society for regulation to address the problem. But the sentiment in Warme’s statement is only now beginning to surface as a political issue – namely, that human connection is something that must be preserved and cannot be replaced by AI. No mental health counseling, no sex from chatbots The more extreme risks of going too deep with chatbots have been well documented, and Iowa’s law prohibits them from offering mental or psychological health counseling or advice, and requires them to refer any user presenting risks of suicide or self harm to professionals. Chatbots can’t produce sexually explicit visual media, encourage children to engage in sexually explicit acts, or sexually objectify minors. The section of the law addressing the not-human reminder feature specifies that “an operator shall clearly and conspicuously disclose using a persistent visible disclaimer, or a disclaimer
Walton on Thames : live <b>facial recognition</b> used by Police
From Surrey Police Our Live Facial Recognition (LFR) van was deployed in Walton on Thames today as part of ongoing efforts to tackle retail crime, anti-social behaviour and drug-related offences. The operation forms part of our wider commitment to reducing crime, preventing disorder and helping to keep Walton a safe place for everyone. Community engagement is a key part of every LFR deployment. Officers took the opportunity to explain how the technology works, why it is being used, and how it supports our work to identify wanted offenders. The visible presence of the LFR van also acts as a deterrent to crime, and local businesses and residents have told us it helps them feel safer while they are in the area. We recognise that some people have questions about privacy. LFR only alerts officers to individuals on a specific watchlist. If someone is not on that watchlist, their facial image is automatically and immediately deleted and is not retained. If you see the LFR team while we’re out in the community, please come and speak to us. Our officers are always happy to explain the technology, discuss how it is used and answer any questions you may have Leave a comment
Three-headed dogs and long tails: Sophos at BSides LV
In addition to Sophos’ presence at Black Hat USA this year, data scientists from the Sophos AI team will present two talks on novel AI security research at the BSides Las Vegas conference. A three-headed dog On Monday August 3, Adarsh Kyadige presents ‘CerBERTus,’ a three-headed BERT-based model for prompt security. LLM jailbreak detection is often framed as a binary task: is a prompt harmful or benign? However, this framing is brittle. Harmful requests can be concealed inside roleplay, fiction, urgency, or ‘academic’ pretexts, while legitimate prompts can be topically close to unsafe content without malicious intent. As a result, single-label detectors overfit to surface patterns, yielding both false negatives (adversarial rewrites) and false positives (adjacent-benign prompts). CerBERTus addresses this issue because it does not treat jailbreak detection as a flat binary classifier; instead, it models the attacker’s degrees of freedom by disentangling what is being asked (goal) from how it is being asked (frame). The method involves a single shared encoder that feeds three classification heads: - Harmfulness (primary) - Goal category (what the user is trying to do) - Framing style (how the request is presented). To train and stress-test this separation, Adarsh and team built a structured factorial prompt corpus that systematically crosses goals with frames: - Goals include harmful, adjacent-benign, and generic-benign requests spanning categories such as cyberattacks, fraud/social engineering, explosives, chemical/biological weapons, conventional weapons, drug synthesis, privacy/doxxing, human trafficking, extremist propaganda, and racism/nativism. - Frames include adversarial jailbreak styles (e.g., roleplay/persona, screenplay/fiction, urgency/crisis, academic pretext, obfuscation, injection-like prefixes) as well as benign and null/plain framing. The team will demonstrate that the model generalizes to both novel attack goals and novel presentation styles it had not encountered during training. In the talk, they’ll cover the threat model, dataset construction, training objective, and evaluation strategy, and discuss