Face Recognition Technology

The funniest part of “face recognition” is that it doesn’t really recognise faces. Back then we tried to describe faces with hand-made features (edges/textures) and a classifier. It worked… until lighting, angle, motion blur, or a new camera sensor showed up. Now we turn a face into an embedding (a compact list of numbers) and do fast “closest match” search in that number space. It’s basically Google Maps for faces: we’re not comparing photos, we’re comparing coordinates. Where it gets real is the trade-offs: ➤ Fast vs safe: a loose threshold is quick… and lets lookalikes through ➤ Cheap vs smooth: 10× users → bigger indexes + higher GPU bills ➤ Convenient vs secure: deepfakes/replays → liveness checks + least-privilege access One move that saved us: every day we plot the match-distance histogram. Distance = “how close this face is to the stored face” (lower is better). We log two curves: real matches vs random mismatches, plus FAR/FRR (false accept/reject rates). Then we alert on drift. A camera firmware update changed noise/color enough to shift embeddings. Nothing crashed—distances just slid worse. That alert cut our time-to-debug from ~2 hours to ~15 minutes because we stopped guessing and went straight to the camera pipeline. Slide 4 shows the latency split. Slide 7 shows the “fast but wrong” threshold we shipped once. Where have you seen similarity search fail in production, and what guardrail actually prevented the next incident? Follow me, Bhavishya, for AI systems that survive real traffic 🔥 #ml #ai #genai #aiengineer

WHO DOES MY FACE BELONG TO? In November 2019, Kashmir Hill, a new reporter at The New York Times, received a tip about a company called Clearview AI, which was working on facial recognition technology. The tip revealed that Clearview AI had developed a powerful app by gathering billions of photos from social media and other websites. This app could recognize a person’s face and find all their photos online. The tip also mentioned that Clearview AI was selling this technology to law enforcement while trying to keep it secret. Since the 1960s, there have been efforts to make facial recognition technology work, but results were often disappointing. However, Clearview claimed to be different with a “98.6% accuracy rate” and a huge photo database that law enforcement had never had access to before. Hill saw the potential risks of facial recognition technology and started looking into how it affected privacy. Clearview AI’s claims showed improvements in accuracy and effectiveness, but there were serious concerns about privacy violations and ethical issues. Hill’s research became a crucial starting point for understanding the impact of facial recognition technology on society and its future use. During the investigation, Hill discovered how secretive Clearview was and how it monitored its use. Clearview was tracking and blocking searches for photos of reporters like Hill. The company could see who law enforcement was searching for and control the results, showing the power of a secretive company. The book Your Face Belongs to Us(*) explores this topic and examines the development of facial recognition technology and its societal impacts. Kashmir Hill has investigated companies like Clearview AI and the ethical and privacy issues associated with this technology. The book provides a broad view, covering the history of facial recognition technology, its current uses, and its potential future effects. Hill discusses the impacts on security, privacy, and human rights, encouraging readers to consider the challenges and opportunities that come with this technology.   (*) Hill, K. (2023). Your Face Belongs to Us: The Secretive Startup Dismantling Your Privacy, Simon & Schuster, pp. 347.   #technology #facialrecognition #future #privacy #security #cybersecurity  

📕 Regulating facial recognition in the EU - in-depth analysis by European Parliamentary Research Service 🔶 This paper: (1) provides an overview of the technologies, economics and different uses of facial recognition technologies; (2) highlights concerns arising from the technology's specific characteristics and from its potential impacts on people's fundamental rights; (3) takes stock of the legal framework, especially the data protection and non-discrimination rules currently applicable to facial recognition in the European Union; and (4) examines the recent proposal for an EU artificial intelligence act, regulating facial recognition technologies. Finally, the paper briefly looks at the approaches taken to facial recognition regulation outside the EU and at an international level. #facialrecognition #dataprotection #gdpr

Facial recognition in retail isn’t science fiction — it’s shelf-level surveillance in real time. From tracking your frown in aisle three to flagging your face for “loss prevention,” stores are using biometric tools most shoppers never notice and almost no one consents to. But just because the cameras are hidden in the rafters doesn’t mean we should keep our heads down. In my latest edition of #SmokeSignal, I unpack how facial recognition actually works in stores, what’s really being captured, and how your image may be linked to your wallet — and sold downstream. I walk through threats, actionable tools, and state-specific laws you need to know before stepping into the next “smart” store. I also spotlight tools to push back — from Reflectacles to rights-based deletion requests — and caution when to avoid wearing privacy gear altogether. Digital Exhaust is real, but so is digital resistance. We don’t need to fear the smoke if we know where the fire is. Read, share, and let’s turn awareness into action — one face at a time. #FacialRecognition #RetailSurveillance #PrivacyRights #BiometricData #DigitalExhaust #SurveillanceCapitalism #DataPrivacy #ConsumerProtection #SmokeSignal #OptOut #PrivacyTools #DigitalFreedom #BBQNotBigBrother

The Connecticut AG is focused on facial recognition, including by retailers for loss prevention purpose. Here's what's expected ⬇️ The Office of the Connecticut Attorney General recently released a report on its enforcement actions and priorities under the state comprehensive #privacy law. In the middle of the report was detailed guidance about how the AG's office views the use of #FacialRecognition technologies under the state privacy law. The guidance was prompted by a #retail use of facial recognition technology for loss prevention purposes. It notes that the state comprehensive privacy law applies to these uses, and that the crime and fraud exception in the law "is not a blanket exception" to the law's requirements. There "is no 'out' on compliance." It also indicates that facial recognition technology necessarily involves collection, use, and sometimes sharing of #biometrics. The guidance says companies should: 1️⃣clearly disclose use of facial recognition technology and available consumer rights; 2️⃣obtain informed and freely given consent and allow revocation of consent for facial recognition (biometric) data processing; 3️⃣conduct and document a data protection assessment per state law requirements (and re-assess when the facial recognition technology changes or there are new trends in observed data); 4️⃣actively monitor use and performance of the facial recognition technology, including for accuracy in identifications and based on demographic differences; 5️⃣implement strong policies and procedures for data protection assessments, risk-rating, and facial recognition technology-specific bias and discrimination training; 6️⃣address data minimization (limiting the amount of data processed), have clear data retention and deletion procedures, and only process the data for the specific purposes it was collected; and 7️⃣implement a comprehensive #InformationSecurity program with strict access controls, multifactor authentication, and data segmentation for facial recognition data. If your company uses facial recognition technology in consumer contexts in Connecticut, see how your company's practices stack up to these requirements. While some of these requirements like consent and opt-out processes may not be viable in loss prevention or security contexts, others like data protection assessments, security programs, and data minimization and retention practices can still be addressed. If other states take similar views on the applicability of state privacy laws and exceptions, common uses of facial recognition technology in retail and other public-facing contexts may require fresh looks at compliance and risk acceptance. I'm attaching an excerpt of the report that includes the facial recognition guidance. The full report is at https://lnkd.in/gryWMdFM

Australian retailer Bunnings has been cleared to deploy AI facial recognition technology in its stores. The company had been stymied by a 2024 Australian Information Commissioner ruling that the practice was unlawful under Australia’s Privacy Principles (APP), and appealed via the Administrative Review Tribunal, which handed down its decision this week. Read more in this article by Luke Cooper for ABC: https://lnkd.in/guNwS33N What’s the deal? ▪️The tribunal overturned the commissioner’s finding that Bunnings breached privacy principles, accepting that a “permitted general situation” justified the collection of people’s sensitive biometric data without their consent ▪️It found Bunnings had a reasonable belief that facial recognition was necessary to address repeat retail crime and serious threats to staff safety, rejecting arguments that alternatives could achieve the same outcome ▪️It placed weight on proportionality and system design, accepting that the milliseconds-long capture, processing, retention and subsequent deletion, of biometric faceprints of every customer who entered Bunnings stores reduced the privacy impact ▪️However, the tribunal affirmed the commissioner’s view that Bunnings failed to adequately inform customers their sensitive biometric data was being captured, neglected to conduct necessary privacy risk assessments before launching the system, and maintained a privacy policy that made no mention of facial recognition technology. My take ▪️This is the wedge—mass biometric surveillance without consent is now lawful in Australia for preventing crime ▪️While it does not open the door to broad surveillance for other purposes, such as behavioural profiling, it sets an important precedent ▪️Australia doesn’t have AI-specific legislation, so cases like these are likely to have significant watershed impacts ▪️This matters because we know some Australian businesses are still using facial recognition unlawfully, to grow revenue or identify high-value customers in gambling venues, for example; and some are also deploying Chinese AI camera tech that’s banned in the US, but not in Australia ▪️I expect the scope to grow, with deployment of systems designed to predict behaviour and intent, based on inference of visual attributes, and extract maximal commercial value accordingly. What’s next ▪️The commissioner is considering the decision and its implications. An appeal window applies.

One of the most compelling applications of AI, 𝐟𝐚𝐜𝐢𝐚𝐥 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 is rapidly gaining traction worldwide. Countries like the United States, China, and India are integrating facial recognition into public safety initiatives, with advanced deep learning algorithms enhancing its capability to interpret complex scenarios. In China, 𝐭𝐡𝐞 𝐒𝐤𝐲𝐧𝐞𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭, equipped with over 700 million surveillance cameras, has drastically improved crime detection and public security, but it has also sparked global debates on privacy concerns and state surveillance. Similarly, 𝐈𝐧𝐝𝐢𝐚’𝐬 𝐃𝐢𝐠𝐢𝐘𝐚𝐭𝐫𝐚 𝐩𝐫𝐨𝐠𝐫𝐚𝐦 piloted at Delhi Airport, leverages AI-driven facial recognition for seamless passenger verification, showcasing how technology can create more efficient public systems. Additionally, the National Crime Records Bureau is leveraging facial recognition 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐩𝐞𝐫𝐬𝐨𝐧𝐬, 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬 𝐨𝐟 𝐜𝐚𝐬𝐞𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐟 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧. The technology is also transforming retail. Brands like Amazon and Alibaba employ facial recognition in cashier-less stores, enabling consumers to "just walk out" after their purchases. However, 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 persist on the ground. Issues of accuracy and fairness remain pressing. Studies, including a 2019 NIST report, revealed that some facial recognition systems exhibit higher error rates for darker skin tones, highlighting biases in training datasets. This has raised concerns in countries like the United States, where lawsuits and protests have emerged against the use of facial recognition in policing. Ground-level adoption faces practical hurdles in developing nations. In Africa, for instance, countries like Kenya and South Africa are piloting facial recognition to combat urban crime and enhance airport security. 𝐇𝐨𝐰𝐞𝐯𝐞𝐫, 𝐥𝐢𝐦𝐢𝐭𝐞𝐝 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞, 𝐥𝐨𝐰 𝐩𝐮𝐛𝐥𝐢𝐜 𝐚𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬, 𝐚𝐧𝐝 𝐡𝐢𝐠𝐡 𝐜𝐨𝐬𝐭𝐬 𝐨𝐟 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐬𝐥𝐨𝐰 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬. Despite these challenges, the potential of facial recognition remains immense. Japan's Tokyo Olympics showcased its ability to manage security in large-scale global events efficiently. In France, it aids in monitoring major events like the Tour de France. Research from Allied Market Research suggests that by 2030, 80% of security checkpoints globally will integrate facial recognition for better efficiency and safety. In Singapore, The SingPass Face Verification System allows citizens to authenticate their identity for tasks such as filing taxes or accessing public services. This innovation not only streamlines processes but also showcases how AI-driven facial recognition can enhance convenience and security on a national scale. #artificialintelligence #security #faceverificationsystem #airport #camera #globalevents #deeplearning #datascience

It’s time to recognize something about facial recognition ⬇️ Fraudsters are poking holes in it. They have a lot of tools at their disposal: - deepfake generators (like the one below) - app cloners - app tampering tools - image injectors - generative AI - social media full of source photos - liveness spoofing They can use an app tampering tool to inject video from the camera roll. And the app thinks it’s coming from the on-device camera. It doesn’t stop there. The video itself can be a deepfake made (or enhanced) with AI. Facial recognition is highly vulnerable. And its vulnerabilities are getting easier for fraudsters to exploit. Don’t put all your eggs in the biometrics basket. It’s time to start thinking about other, more tamper-resistant signals.

🌟 Day 483: 🤖 Face Recognition with MobileNetV2 in Python Today, I developed a Face Recognition system using Python, combining real-time webcam detection with deep learning classification powered by Transfer Learning with MobileNetV2. The goal was to build a robust model capable of identifying multiple individuals from live video input. Here's what I accomplished: - Data Preparation: I built a custom face dataset with images labeled for each person. The images were processed with OpenCV to detect and crop faces, ensuring consistent input for the model. - Transfer Learning: I implemented MobileNetV2 pre-trained on ImageNet, adding dense layers and Dropout to fine-tune the model for the face recognition task while reducing overfitting. - Data Augmentation: To improve generalization, I applied transformations like rotation, zoom, brightness adjustments, and horizontal flipping during training. - Real-Time Prediction: The system captures video feed from the webcam, detects faces in real-time, and classifies them with a confidence threshold to avoid unreliable predictions. Despite these advancements, I’m still facing a recurring challenge: the system consistently detects myself (Caleb) with a confidence of 1.0, but struggles to accurately recognize Kobe and Freeman, especially when using images from Google. This has highlighted a domain gap between my training dataset and the test images, and I’m exploring ways to overcome this. I’d appreciate any guidance or insights on how to improve the model’s generalization across different sources and reduce this bias. Check out the project on GitHub: https://lnkd.in/e7Y4AaDR #Python #DeepLearning #ComputerVision #FaceRecognition #TransferLearning #MachineLearning #LearningJourney #Tech Always excited to tackle new challenges and grow through this journey 🚀

Finally – AI doing something useful for education! 🎓 I’ve been working on an AI-powered attendance system that can automatically detect students from classroom videos and mark their attendance with 92%+ accuracy ✅ We’ve all seen AI generating art, text, or chat responses. But here’s AI actually solving a real problem that teachers and institutions face every single day. Manual attendance takes time and is often error-prone. This system makes it fully automated! 👉 How it works Input: Classroom video + student dataset Detect & recognize faces using InsightFace (RetinaFace + ArcFace) Build embeddings, match with roster, and generate: ✔ Annotated video ✔ Attendance summary (CSV) ✔ Absent list (TXT) 👉 Technologies Used Python (OpenCV, Pandas, TQDM, Dataclasses) InsightFace (RetinaFace + ArcFace) for face detection & recognition ONNXRuntime (GPU) for fast inference NumPy & CSV processing for embeddings & reports The current prototype is already working well with videos, but my vision is bigger: ✨ Inshaa-Allah, this is just the first step. Next, I’ll take it to the next level so attendance can be taken directly from live classroom cameras — without teachers needing to do it manually. This is the kind of AI revolution I want to see — not replacing teachers, but helping them by saving time and reducing errors. #AI #ComputerVision #DeepLearning #Automation #EdTech #FaceRecognition