Facial Landmark Detection and head pose compute use dlib, Real time Face Reconstruction use 3D Morphable Face Model fitting

This project detects cheating by tracking head and pupil movements using the Shape Predictor 68 facial landmarks. It also detects mobile phones with a YOLO model trained on the Roboflow cellphone detection dataset. Designed for real-time monitoring, it helps prevent cheating by analyzing facial cues and unauthorized device usage.

Real time facial landmark detection with Tensorflow.JS accompanying YT vid: https://youtu.be/PyxsziqatFE

Real-time facial landmarks detection / 摄像头人脸检测并进行特征点标定

Detect eye blinks based on eye aspect ratio (EAR) introduced by Soukupová and Čech in their 2016 paper, Real-Time Eye Blink Detection Using Facial Landmarks.

my unofficial implementation of PFLD paper "A Practical Facial Landmarks Detector" for a real time landmarks detection & head pose estimation

Real-Time Blendshape Manipulation with Facial Landmark Detection from 2D Video

This project detects eye blinks in real-time using Eye Aspect Ratio (EAR) and MediaPipe's facial landmark detection. It draws eye landmarks, calculates the EAR, and plots the results in real-time.

👁️ Eye-controlled virtual keyboard using computer vision and facial landmark detection. Features dual keyboard layouts, blink detection for character selection, gaze tracking, audio feedback, and real-time visual interface. Built with OpenCV, dlib, and Python.

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This repository contains the jupyter and mathematica notebooks of the Transfer Learning Model on VGG16, ResNet 50 architecture with ImageNet weights and FaceNet, and also our code proposed of the technique anti spoofing in the paper "Face Liveness Detection Based on Perceptual Image Quality Assessment Features with Multi-scale Analysis". Our project is about a Transfer Learning CNN on VGG16 architecture because it has the best results with an accuracy of 93,016 % in CASIA dataset, 97,321 % MDP dataset and 91,142 % NUAA dataset, we use a "real-time" landmark detection however the transfer learning model and landmark detection don't work together yet (our future work is that these two technologies can work together to reduce errors and improve the accuracy). This also contains the full paper of the "Anti Spoofing Face Detection Technique based on Transfer Learning Convolutional Neural Networks and Real-Time Facial Landmark Detection" to the Latinx in AI - ICML Workshop call.

Every year many people lose their lives due to fatal road accidents around the world and Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Alcohol, Overwork, Stress, and even Medical conditions can cause drivers to fall sleep. It is very important to detect the drowsiness of the driver to save life and property. So to reduce the accidents and save the life of a driver we propose to develop a system called as Driver Drowsiness Detection (D3 ) system. This system can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy. Haar Cascade classifier, facial landmarks and computing Eye Aspect Ratio (EAR) to ensure proper detection of drowsiness in order to avoid accidents. For implementing this system we used libraries like Opencv and dlib.

This project implements a real-time drowsiness detection system that uses a webcam feed to monitor eye activity to determine if a person is active, drowsy, or asleep. It leverages the OpenCV library for image processing, dlib for face detection and facial landmark prediction, and imutils for handling facial landmarks.

Modern facial motion capture systems employ a two-pronged approach for capturing and rendering facial motion. Visual data (2D) is used for tracking the facial features and predicting facial expression, whereas Depth (3D) data is used to build a series of expressions on a 3D face models. An issue with modern research approaches is the use of a single data stream that provides little indication of the 3D facial structure. We compare and analyse the performance of Convolutional Neural Networks (CNN) using visual, Depth and merged data to identify facial features in real-time using a Depth sensor. First, we review the facial landmarking algorithms and its datasets for Depth data. We address the limitation of the current datasets by introducing the Kinect One Expression Dataset (KOED). Then, we propose the use of CNNs for the single data stream and merged data streams for facial landmark detection. We contribute to existing work by performing a full evaluation on which streams are the most effective for the field of facial landmarking. Furthermore, we improve upon the existing work by extending neural networks to predict into 3D landmarks in real-time with additional observations on the impact of using 2D landmarks as auxiliary information. We evaluate the performance by using Mean Square Error (MSE) and Mean Average Error (MAE). We observe that the single data stream predicts accurate facial landmarks on Depth data when auxiliary information is used to train the network.

This project uses computer vision to track eye pupils in real-time, enabling precise mouse cursor control. It employs MediaPipe for facial landmark detection and OpenCV for video processing, facilitating accurate interaction for users, especially those with disabilities.

The Real-Time Eye Blink Detection and Counting project uses OpenCV and dlib to detect and count eye blinks in real-time through a webcam feed. It utilizes dlib's facial landmark detection model to locate key points around the eyes and calculates the blinking ratio.

Indranav is a real-time computer vision–powered web application built with FastAPI and React, leveraging dlib’s 68-point facial landmark model and OpenCV for precise facial feature detection and analysis. It combines ML-driven image processing and modern TypeScript + Tailwind UI

The Driver Drowsiness Detection System uses computer vision to monitor facial landmarks and blinking patterns in real-time, alerting drivers to their alertness status to prevent accidents due to drowsiness.

Welcome to our drowsiness detection project using Python and the powerful dlib library! 😴 This repository hosts a user-friendly implementation to detect drowsiness in real-time using your computer's webcam. Our easy-to-understand Python code utilizes the dlib library's advanced facial landmark detection to monitor facial cues and alert users when

FocusTrack is a smart productivity companion that uses real-time computer vision and behavioral analytics to monitor user attention, screen time, and focus patterns. By leveraging facial landmark detection, head pose estimation, and eye-blink/mouth movement analysis, FocusTrack detects signs of distraction, drowsiness, and cognitive fatigue.

This project utilizes MediaPipe and OpenCV to perform real-time face mesh detection using a webcam feed. The program captures video frames and processes them using the MediaPipe Face Mesh module to detect facial landmarks and contours.

A toolkit for face detection, annotation, emotion, age, gender, mask detection, and real-time facial landmark visibility.

A real-time face tracking and captioning application built with React, TypeScript, and MediaPipe. This project demonstrates advanced facial landmark detection combined with speech-to-text capabilities to create an augmented reality captioning experience.

Real-Time webcam-based application utilizing facial landmark detection to dynamically apply diverse face filters, including blur, sunglasses, mustache, etc.

PyBlink is a Python script designed for real-time blink detection in live video streams. Leveraging the power of the dlib library and OpenCV, this script identifies facial landmarks, specifically focusing on the eyes, to detect blinks accurately.

This project is a real-time drowsiness detection system designed to monitor a user's eye activity and alert them if signs of drowsiness or sleep are detected. It leverages computer vision techniques and facial landmark detection to analyze eye blinking patterns, ensuring the safety of drivers or individuals performing critical tasks.

Face Tracker with Eye Detection is a Python computer vision project using OpenCV and MediaPipe Face Mesh to detect and track multiple faces in real time through a webcam. It draws facial mesh connections and highlights eye landmarks, demonstrating real-time AI-based facial tracking and visualization.

This Repo contains Facial-Landmark-Detection using opencv ,numpy and dlib . In this Project, I have implemented Facial landmarks(key points) detection system using Convolution Neural network and image processing techniques. Facial landmark detection is regression kind of task where output is a set of values representing positions in the image . This project uses opencv , numpy and dlib libraries. I had used pre-trained model for the landmarks detection. For any task of processing facial features on real-time images, first step will be detecing the faces in the image. Face detection task is achieved by using dlib's implementation. Once faces are detected, we will feed them to the trained model to predict the landmarks.

FaceAPI.js React App is a real-time face detection and recognition application built with React and powered by FaceAPI.js. It allows users to detect facial landmarks, analyze expressions, and assess soft skills based on facial cues, without the need for downloading models.

A real-time vision system for 241-252 Intelligence Machine Vision Module, using OpenCV for facial landmark detection and expression recognition. It applies a PS2-style cartoon effect and overlays comic bubbles based on detected emotions. Designed for AI-powered interactive visuals.