Real-time face detection using OpenCV, Node.js, and WebSockets

Real-time face detection using WebRTC, OpenCV, Node.js, and WebSockets

Face Detection With HTML5's WebSockets, WebRTC's getUserMedia() and OpenCV on Node.js

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👤 Node.js face detection library based on OpenCv

Face Detection ROS Node using OpenCV

a zoom clone written with node.js and enhanced by applying computer vision and neural network models to detect face emotion of students to get continuous feedback of students improvement and online learning methods written in node.js, Mongo Db, Express, React(for front end) and python( numpy, opencv, Keras, Tensorflow) for the face detection engine

:camera: Realtime Webcam Face detection in Nodejs using opencv4nodejs

Face detection and cropping node for ComfyUI using OpenCV Haar cascades. Supports v3 schema with backward compatibility for v1/v2 workflows.

node providing OpenCV's face detection with the PAL specific message

Node.js wrapper for OpenCV Face Detection using Haar Cascades

Realtime face-detection using node-opencv (node.js bindings for OpenCV), socket.io, and HTML5 Canvas

ROS Node that uses OpenCV 2.4 to perform face detection and recognition. Listens for published images from a Kinect or webcam, performs image analysis, and tells the robot where to turn its head to follow the individual.

QUASSAR (codename QSR-101) is an advanced quadruped robot aimed to provide functions in support, surveillance and education in multiple environments, industrial, corporate, home and lot more. It features advanced sensory systems and computational power for all of its functions. It implements a computational grid distributed in 3 nodes / boards with the following characteristics: Cortex Node Samsung Exynos5422 Cortex™-A15 1.8Ghz Quad-Core and Cortex™-A7 Quad-Core CPUs (Octo-Core) * Mali-T628 MP6(OpenGL ES 3.0/2.0/1.1 and OpenCL 1.1 Full profile) * 2Gbyte LPDDR3 RAM at 933MHz (14.9GB/s memory bandwidth) PoP stacked * eMMC5.0 HS400 Flash Storage Motion Node SoC Broadcom ARM® Cortex®-A7(ARMv7) 0.9Ghz Quad-Core CPU * Mali™-450 MP2 GPU (OpenGL ES 2.0/1.1 enabled for Raspbian) * 1Gbyte DDR3 SDRAM Support Node 8-bit Microcontroller Board (designed and built by me) running at 40 MHz. 4KB of internal RAM. ADC S/H for the Front & Read proximity sensors (pseudo-Lidars) PWM & Control for the front Ilumination Unit The system configures a "Grid" architecture on which, the core nodes are connected through Ehternet LAN at 100Mbps, and the Support node is accessed over I2C. The main nodes are remotely accessed over WiFi independently, but both are continuously interchanging information through the LAN network. It features two "pseudo-lidars" front and rear to perform near object detection. These sensors continuosly scan the front and rear perimeter to provide accurate information about surrounding objects. Special software patterns and styles based on RTOS architecture have been implemented. A complete and proprietary mathematical model has also been designed to provide all of the IK functions. It features advanced object and face recognition functions based on OpenCV, by using classifiers (Haar Cascade Classifier). It also will implement advanced AI and voice processing features based on well-known libraries like Google's TensorFlow, BlueMix, Fann etc.

face detection using opencv4nodejs

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Face detection using Node.js and OpenCV

A face detection node js application using opencv

Node.js wrapper for OpenCV Face Detection using PICO

Implements face detection using node-opencv as Lambda function

A simple example on opencv face detection with node.js

Real Time Face Detection using Node.js OpenCV and Websockets

Face and Age detection using Python OpenCV module and Node.js for server

ROS NODE FACE DETECTION. http://docs.opencv.org/trunk/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html#cascade-classifier

collection of custom nodes for ComfyUI that provide advanced face callout, annotation, and compositing effects using OpenCV and PIL. These nodes are designed for image processing workflows that require face detection, annotation, and creative compositing.

This ROS package contains a node that detects faces using the laptop's integrated camera. It captures video streams, processes frames with OpenCV, and identifies faces in real-time. The package is ideal for simple face detection tasks and can be integrated with various ROS-based applications.