HTML5 Video Face Detection with the CCV Javascript Library

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

A Simple Face Detection Web App built using Javascript, HTML5 and Face-API JS

Face detection with HTML5 and multiple images

Real-time detection and identification of user's faces using Facenet architecture (tensorflow), with video transcoding to HTML5 compliant webm/VP8 (ffmpeg).

Face detection in JavaScript with HTML5 Canvas API.

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

HTML5 webcam face detection

My face detection application built using REACT, JS, NODE, HTML5, CSS3

Playing around with some face detection and html5 canvas in times of covid-19

A hands-free gestural interface and AR face-tracking engine built with Handsfree.js and HTML5 Canvas. Features real-time landmark detection and vector-based gesture recognition.

No description available

No description available

No description available

I have used face detection api and models for face detection and coded it by using JS and html5 .

This is the front-end of the Face Recognition app using images created using Javascript, React.js, CSS, HTML5 and the Clarifai face detection API.

Languages & Technologies: HTML5, CSS3, JavaScript, React, Particles.js, Tachyans.js, Clarifai API Using the Clarifai API for face detection, this web application allows the user to upload a picture of someone and the app will draw a square indicating the face.

A face detection website made using HTML5, EJS, SCSS, Javascript, and Express Web Framework. It uses the Clarifai actor/actress AI model to recognize the actor/actress in a given photo.

Developed a Chrome extension using React and IDfy’s Face Liveness API to detect and verify live human faces via webcam. Implemented camera access, image capture, and liveness detection using the HTML5 getUserMedia() API. Integrated real-time image analysis to enhance security and prevent fraudulent identity verification.

This is a real-time motion detection and recording system built using Flask, OpenCV, and HTML5 video streaming. The app captures live video from your system's webcam, detects human motion using face and full-body classifiers, and automatically records short clips when movement is detected.

Project demonstrate 2D game using HTML5 and vanilla JavaScript . added sound to the game, to make player character follow the mouse whenever we click somewhere, to handle simple collision detection that triggers sprite animation and to rotate our fish sprite correctly to face the direction the player is moving. moreover cover repeating backgrounds, handle score and couple of other things. First I build a solid good quality game skeleton that can be used for more than just this particular fish game. besides start adding graphics with sprites, particle effects and make the game look polished and clean.

Today everyone is facing major security issues with their accounts over the internet; consequently, they need several specially trained personnel to attain the desired security. These personnel, as human beings, make mistakes that might affect the level of security. A proposed solution to the aforementioned matter is a Face Recognition Security System-based login, which can detect intruders and restrict them from logging into other accounts with stolen credentials. This system is composed of two parts: the hardware part and the software part. The hardware part consists of a camera, while the software part consists of face-detection and face-recognition algorithms software. When a person tries to log in to his/her account then after intaking login credentials, a series of snapshots are taken by the camera and sent to the software to be analyzed and compared with an existing database of the same person. If the person’s face pattern is matched with database patterns then the person is logged into the account else the person is restricted from logging in. The person's snaps were taken and stored in the database. The programming language Python is used to write the backend software that drives the face patterns and analyzes the same with input snaps. And the main concept here is, Siamese Network, which is a One-shot classifier with two mirror-image subnetworks to rank similarity or dissimilarity between the two inputs using Similarity score For Facial Recognition, you will input two facial images to the Siamese network and pass through two simulations that used to recognize the frontal face of a person and the in-built modules in python like OpenCV which is also known as Computer Vision that used to chunk the capturing video into no. of frames that have been taken input from webcam and face recognition are the supreme of the entire software. The frontend is integrated as a web application that is built on web technologies like HTML5, BootStrap5, CSS3, Java script, and a web framework “Flask”, that efficiently works as a medium between frontend and backend.