This article explains how to build a cost‑effective, CPU‑only face verification system for small‑scale projects using the lightweight YuNet detector and SFace recognizer, covering the models’ principles, implementation steps with OpenCV and Gradio, and performance considerations.
This guide walks through installing OpenCV and php-facedetect, writing PHP code to capture webcam images, crop them, run Pico face detection, and overlay bounding boxes, providing a complete example for object detection using PHP.
This tutorial guides PHP developers through installing OpenCV and the php‑opencv extension, capturing live video, displaying frames in a browser, and performing real‑time face detection using Haar cascades, providing a practical introduction to computer‑vision tasks in PHP.
This tutorial explains how to implement face, eye, and mouth detection using OpenCV's Haar cascades, Dlib's HOG and CNN detectors, and provides step‑by‑step code for both static images and real‑time video streams in Python.
This article explains how to install OpenCV and php-facedetect libraries, write PHP code to capture images from a webcam, perform face detection using the pico library, and display the results, providing a step‑by‑step guide for object detection with PHP.
This tutorial explains how to install OpenCV and php-facedetect, write PHP code to capture images from a webcam, perform face detection, and display the results, providing step‑by‑step commands and a complete example script.
This article explains the fundamentals of face detection using the Haar‑cascade algorithm, how to train and apply detectors with OpenCV, and introduces the Local Binary Patterns Histograms (LBPH) method for face recognition, covering data preparation, parameter selection, and matching techniques.
This guide provides a comprehensive walkthrough of installing OpenCV, using its Java API for image and video face detection, implementing face comparison, creating custom GUI windows, and integrating the library into a Spring Boot application with detailed code examples and common troubleshooting tips.
This tutorial explains how PHP developers can install OpenCV and the php‑opencv extension, capture video from a webcam, display live frames in a browser, and perform basic real‑time image processing such as face detection using Haar cascades.
This tutorial explains how to install required libraries, write PHP code that captures images from a webcam, uses OpenCV and php‑facedetect to detect faces, and displays the results with annotated bounding boxes, providing a foundation for further object detection projects.
This tutorial explains how PHP developers can install OpenCV and the php‑opencv extension, capture video from a webcam, display live frames in a browser, and perform basic real‑time image processing such as face detection using OpenCV’s cascade classifier.
This article explains how to collect images from Zhihu's "beauty" topic using Python's Requests and lxml libraries, filter them with Baidu AI's AipFace face detection service, and store the qualified pictures locally, detailing the required environment, logic, and preparation steps.
Liu Jun explains how Bilibili’s engineers replaced traditional pre‑processed SVG masks with a real‑time, browser‑based machine‑learning pipeline—using MediaPipe SelfieSegmentation, OffscreenCanvas, and Web Workers—to extract human contours and block faces in danmaku, achieving roughly 5 % CPU load on a 2020 M1 MacBook.
This article reviews the evolution of face detection, covering the Widely‑Face dataset, major research directions such as feature fusion, label assignment, auxiliary supervision, anchor‑free methods, NAS‑based designs, summarizes key papers from S3FD to MogFace, introduces ModelScope implementations, and outlines future challenges and opportunities.
This article explains how to use ModelScope's Model-as-a-Service for face detection, demonstrates the required Python code, and shows how to build and share a Gradio‑based AI application, including a more complex "Time Album" use case.
This tutorial explains how to create a simple virtual avatar system by combining convolutional neural networks, the face‑api.js library, and WebRTC, covering CNN fundamentals, face detection, landmark extraction, model selection, and rendering techniques with code examples.
This tutorial shows how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu to fetch WeChat friend data and then visualize gender distribution, detect face usage in avatars, generate word clouds and sentiment analysis for signatures, and export location information for mapping.
This guide explains how to collect images from Zhihu’s “美女” topic using Python’s Requests and lxml, filter them with Baidu’s AipFace SDK based on face presence, gender, authenticity, and beauty score, and store the qualified pictures locally.
This tutorial explains how to use Python 3 with Requests, lxml, and Baidu's AipFace SDK to crawl images from Zhihu's "beauty" topic, filter them by face detection and gender criteria, and store the qualified pictures locally.
This article details the design, technical choices, and iterative optimizations of a large‑scale political face detection and recognition system, covering face detection, alignment, and feature extraction modules, performance improvements, deployment results, and future challenges.
This article introduces the fundamentals of face detection using Python's OpenCV library, explaining the underlying concepts of computer vision and providing a complete, step‑by‑step script that loads an image, converts it to grayscale, detects faces with a Haar cascade, and visualizes the results.
This article demonstrates how to install the Python face_recognition library, locate faces in images, crop and save them, encode faces into 128‑dimensional vectors, compare faces, detect facial landmarks, apply virtual makeup, and assemble these steps into a simple custom face‑recognition application.
This tutorial shows how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu to fetch WeChat friend data and produce visual analyses of gender distribution, avatar face detection and tags, signature word clouds and sentiment, and geographic location, all illustrated with charts and code examples.
Didi’s COVID‑19 mask‑detection system, built on a DFS‑based face detector and an attention‑enhanced ResNet‑50 mask classifier achieving over 99.5 % accuracy, has been deployed in vehicles, open‑sourced, and complemented by top‑ranked results in international visual AI contests, including first place in driver‑gaze prediction and podium finishes in emotion recognition and model‑compression challenges.
This article explains the principles and implementation of face detection based on statistical methods, detailing Haar feature types, integral image computation, feature normalization, cascade classifiers, and provides step‑by‑step OpenCV code examples for static images, eye detection, and real‑time webcam detection.
This article explains how UCloud’s AI team leveraged the UAI‑Train and UAI‑Inference platforms to develop, train, and deploy a high‑accuracy face‑mask detection service within a week, detailing the algorithmic approach, challenges, deployment pipeline, and real‑world applications.
VICFace, a Meituan Vision Intelligence Center model, combines advanced data augmentation, a ResNet‑152‑based SRN architecture, specialized anchor design, and multi‑task loss functions to achieve state‑of‑the‑art face detection accuracy on WIDER FACE, enabling robust natural‑scene applications such as content filtering and safety checks.
This article provides an in‑depth review of face detection, covering traditional knowledge‑, model‑, feature‑ and appearance‑based approaches, modern deep‑learning methods such as cascade CNN, MTCNN and Facebox, strategies for handling multi‑scale faces, anchor‑box densification, and practical training considerations.
This article explains how to crawl Zhihu's "beauty" topic images using Python, Requests, lxml, and Baidu's AipFace SDK, filter them by gender, face presence, realism, and beauty score, and store the high‑quality results locally.
This article explains why AI‑generated faces from StyleGAN are hard to distinguish, introduces an online game for testing realism, and provides practical visual cues—such as water spots, background errors, asymmetric glasses, hair artifacts, and teeth anomalies—to reliably identify fake images.
JD AI Research Institute introduced the Improved Selective Refinement Network (ISRN), a face‑detection model that outperforms all competitors on the WIDER FACE benchmark across Easy, Medium, and Hard subsets with AP scores of 96.3%, 95.4% and 90.3%, thanks to novel training strategies, COCO pre‑training, redesigned input modules, and multi‑stage classification‑regression enhancements.
This article introduces the Intelligent News Formatter, an AI‑driven system that tackles news‑app list‑image problems by using face detection, object detection, deep‑learning based cropping, image quality filtering, and similarity removal to automatically produce aesthetically pleasing and information‑rich thumbnails.
This guide explains how to collect images from Zhihu’s “美女” topic using Python’s Requests and lxml, filter them with Baidu’s AipFace API based on gender, face presence, authenticity, and beauty score, and store the high‑quality results locally, including setup and optional customizations.
This article showcases five engaging Python projects—including drawing a national flag with Turtle, converting images to ASCII art, building a Tetris clone, creating a simple plane‑shooter game, and implementing a 25‑line face‑detection script—complete with code snippets, installation tips, and visual results.
This article explores how user empathy and face‑detection technology guided the design of photo‑editing and sticker features in the “LaoXiangShuo” module of the Ganji app, targeting post‑90s users with tailored visual and textual enhancements.
