# face recognition Published articles for face recognition. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quiz: Build Your Own Face Recognition Tool With Python DevFeed: [Quiz: Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-05T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python]() Tags: [command-line](), [face-recognition](), [how-to](), [python](), [recognition](), [tool](), [tutorial](), [validation]() ### AI overview An interactive 10-question quiz that tests understanding of a Python face recognition tool, including face detection versus recognition, face encodings, voting-based matching, model validation, bounding box coordinates, and argparse command-line options. ### Source excerpt Check your understanding of face detection, face recognition, face encodings, bounding boxes, and model validation in Python. ## The New UGREEN HomeAgent Series Revealed DevFeed: [The New UGREEN HomeAgent Series Revealed]() Original publisher: [Read original article]() Author: Rob Andrews Published: 2026-09-03T22:00:40Z Content type: news Language: en Sources: [NAS Compares]() Topics: [cloud-infrastructure](), [Automation](), [servers](), [Artificial Intelligence](), [Hardware](), [smart speaker]() Tags: [070-tflops-ai](), [10gbe-nas](), [13-3-inch-smart-frame](), [2](), [2-5gbe-nas](), [20-tops-npu](), [205-tops-ai](), [4k-indoor-camera](), [4k-outdoor-camera](), [9x-hybrid-zoom](), [ai](), [ai-accelerator](), [ai-event-summaries](), [ai-keyword-search](), [ai-nas](), [announcement](), [arm-nas](), [armv9-nas](), [automation](), [battery-security-camera](), [bluetooth-smart-home](), [cix-p1](), [cix-p1-cp8180](), [colour-e-paper-frame](), [e-ink-spectra-6](), [edge-ai](), [face-recognition](), [ha100](), [ha100-price](), [ha100-pro](), [ha100-pro-price](), [hardware](), [home-assistant-compatible-devices](), [server](), [smart-speaker](), [uncategorised]() ### AI overview UGREEN revealed the HomeAgent series, a family of locally managed systems combining storage, AI processing, surveillance, voice interaction and home automation. The broader range includes central systems, cameras, a smart speaker and an E-Paper photo frame. Software remains under development, and the products are initially being introduced through crowdfunding. ### Source excerpt The New UGREEN HomeAgent Series Revealed UGREEN has spent the last few years building out its NAS range, but its latest announcement takes the company in a rather different direction. I was in Boston for the reveal of UGREEN HomeAgent, a new family of systems designed to bring local storage, AI processing, home surveillance and [...] ## ESP-WHO: Get started DevFeed: [ESP-WHO: Get started]() Original publisher: [Read original article]() Author: John Lee Published: 2026-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Tutorial](), [Computer vision](), [Image processing](), [ESP32-S3](), [ESP-IDF](), [Inference](), [Hardware](), [Framework](), [Development]() Tags: [blog](), [camera](), [computer-vision](), [dl](), [esp-idf](), [esp-who](), [esp32](), [esp32-s3](), [face-recognition](), [hardware](), [image-processing](), [inference](), [led](), [tutorial]() ### AI overview This tutorial explains how to set up ESP-WHO on the ESP32-S3-EYE board, run a face recognition example, and extend it with custom detection callbacks. It covers the platform architecture, component pipeline, hardware abstraction, prerequisites, and an LED response when a face is detected. ### Source excerpt In this article, we will set up ESP-WHO on the ESP32-S3-EYE board, running a face recognition example, and extending it with custom detection callbacks. ## ESP-SparkBot：Large Language Model Robot with ESP32-S3 DevFeed: [ESP-SparkBot：Large Language Model Robot with ESP32-S3]() Original publisher: [Read original article]() Author: John Lee Published: 2025-04-23T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-S3](), [Hardware](), [Artificial Intelligence](), [Language models](), [Development]() Tags: [artificial-intelligence](), [blog](), [debugging](), [esp-now](), [esp32-s3](), [face-recognition](), [facial-recognition](), [hardware](), [home-automation](), [language-models](), [motion-detection](), [offline-speech-recognition](), [usb-screen-mirror](), [video]() ### AI overview This article introduces ESP-SparkBot, a low-cost, multifunctional AI robot built with ESP32-S3. It describes the robot's voice interaction, facial recognition, remote control, motion detection, multimedia capabilities, hardware design, power options, and supporting software framework. ### Source excerpt This article provides an overview of the ESP-SparkBot, its features, and functionality.It also details the hardware design and outlines the software framework that supports its operation. Introduction# With the booming development of generative artificial intelligence, large language models (LLMs) are becoming a core technology in the AI field. ## Building Intelligent Apps with Apple AI Models DevFeed: [Building Intelligent Apps with Apple AI Models]() Original publisher: [Read original article]() Published: 2024-09-18T00:00:00Z Content type: tutorial Language: en Sources: [Kodeco | High quality programming tutorials: iOS, Android, Swift, Kotlin, Unity, and more]() Topics: [AI Development](), [Machine learning](), [object-detection](), [real-time](), [App]() Tags: [ai](), [app](), [apple](), [computer-vision](), [course](), [face-recognition](), [framework](), [machine-learning](), [object-detection](), [on-device](), [real-time](), [use-cases]() ### AI overview A learning path about building intelligent apps with Apple AI Models. It covers on-device machine learning, computer vision with the Vision framework, real-time translation with the Translation framework, and customizing pre-built models with Create ML. ### Source excerpt This course explores on-device machine learning using Apple's powerful tools. See how simple the Vision framework makes complex computer vision tasks, enabling your app to understand the real world, through tasks like object detection and face recognition. Learn to leverage the Translation framework for on-device, real-time language translation, breaking down language barriers for your users. Before finally looking at how to develop your own machine learning models, by customizing Apple's pre-built models for specific use cases within your apps. ## Hacking FaceNet using Adversarial examples DevFeed: [Hacking FaceNet using Adversarial examples]() Original publisher: [Read original article]() Author: Suresh Alse (noreply@blogger.com) Published: 2018-01-11T23:29:00Z Content type: tutorial Language: en Sources: [Life Plus Linux]() Topics: [face recognition](), [Machine Learning, Security Attacks](), [Deep learning](), [Neural Network](), [Authentication]() Tags: [authentication](), [deep-learning](), [deeplearning](), [embedding](), [face-recognition](), [hacking](), [ml](), [neural](), [security]() ### AI overview This tutorial explains how FaceNet performs face recognition using embeddings and triplet loss, then demonstrates generating small adversarial noise intended to make an attacker's photo be identified as a target. ### Source excerpt With the rise in popularity of face recognition systems with deep learning and it's application in security/ authentication, it is important to make sure that it is not that easy to fool them. I recently finished the 4th course on deeplearning.ai where there is an assignment which asks us to build a face recognition system - FaceNet. While I was working on the assignment, I couldn't stop thinking about how easy it is to fool it with adversarial examples. In this post I will tell you how I managed to do it. First off, some basics about FaceNet. Unlike image recognition systems which map every image with a class, it is not possible to assign a class label to every face in face recognition. This is because one, there are way too many faces that a system should handle in the real world to assign class to each of them and two, if there are new people the system should handle, it can't do it. So, what we do is, we build a system that learns similarities and dissimilarities. Basically, there is a neural network similar to what we have in image recognition and instead of applying softmax in the end, we just take the logits as embedding for the given image input and then minimize something called the triplet loss. Consider face A, we have a positive match P and negative match N. If f is the embedding function and L is the triplet loss, we have this: Triplet loss Basically, it is incentivizing small distance between A - P and large distance between A - N. Also, I really recommend watching Ian Goodfellow's lecture from Stanford's CS231n course if you want to know about adversarial examples. Like I said earlier, this thought came to me while doing an assignment from 4th course from deeplearning.ai which can be found here and I have built on top of it. The main idea here is to find small noise that when added to someone's photo although causing virtually no visual changes, can make faceNet identify them as the target. Benoit (attacker) Add noise Kian Kian Actual (Target) First l