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.

GazeCraze is a modular Python application that uses computer vision and facial landmark detection to analyze user concentration in real-time. It leverages MediaPipe Face Mesh, OpenCV, and head/eye tracking techniques to infer gaze alignment and focus level from webcam video streams.

The real-time face landmark detection system in Python was developed using the OpenCV library. Face detection is a computer technology used in a variety of applications to identify human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.

Real-time face mesh detection project using OpenCV and MediaPipe in Python, providing detailed 3D facial landmark tracking and visualization capabilities.

Facial Landmark Detection in Python, using OpenCV for real-time video capture and basic face detection. This project lays the groundwork for an advanced facial landmark model, utilizing a pre-trained face detection model and a custom-trained CNN for accurate facial landmark identification.

Real-time face mesh detection in Python using Mediapipe and OpenCV. Supports webcam and video file input, displays 468 facial landmarks, and provides modular, customizable code for facial analysis and AR applications.

Real-Time Facial Emotion RecThis project demonstrates real-time facial emotion detection using Python, OpenCV, and Media Pipe’s Face Mesh. It captures live video from your webcam, detects facial landmarks, and infers the user’s emotion based on geometric features displaying both an animated avatar and an emoji label in real time

Real-time facial and hand landmark detection using Python and OpenCV. Detects faces and hands in webcam feeds, tracing key landmarks for each detected face and hand in real time.

Real-time face detection system using MediaPipe and OpenCV in Python, enabling fast and accurate facial landmark detection for computer vision applications.

Detects 468 face landmarks in real time as well as when video is uploaded. Tools used- OpenCV, Media Pipe, Python. Application- Emotion detection, Face tracking

this project use OpenCV and cvzone for Hand Detection, FaceMesh Detection, Hand Detection and finaly Pose Detection in real-time . Features : Real-time hand, face, and pose detection., Displaying landmarks and body keypoints., Gesture interaction with the detected objects. Requirements: Python 3.x, OpenCV, cvzone

Real time facial landmark detection in an image using OpenCV, python and dlib. Facial landmarks are used to localize and represent salient features of the face such as eyes, eyebrows, nose , mouth, jawline.

This project demonstrates real-time face mesh detection using the MediaPipe library and OpenCV in Python. The application captures video from a specified source, detects face landmarks, and overlays a face mesh on the detected faces.

**Face Detection using MTCNN and Deep Learning**: This project demonstrates real-time face detection using the MTCNN model and Deep Learning. It detects multiple faces, identifies facial landmarks, and draws bounding boxes in webcam feeds or images. Built with Python, TensorFlow, OpenCV, and MTCNN. Contributions are welcome!

Developed a face detection system using MTCNN to accurately detect faces and facial landmarks in images and videos, ensuring high precision and real-time applicability. – Implemented the solution using Python and OpenCV, optimizing it for applications in surveillance, facial recognition,and emotion analysis.

FaceSnapCV is a Python-based project that uses OpenCV and Mediapipe to detect faces and facial landmarks in real-time via webcam. It can serve as a starting point for face recognition, emotion detection, or AR filters.

Python-Gaze-Face-Tracker is a Python-based application designed for advanced real-time eye tracking, facial landmark detection, and head tracking, utilizing OpenCV and MediaPipe technology. Specializing in uncaliberated gaze tracking, this tool is an easy to use Python eye and facial landmark tracker.

Real-time Drowsiness Detection system implemented in Python using OpenCV for video capture and the dlib library for face and facial landmark detection. It monitors the Eye Aspect Ratio (EAR) to determine the state of the eyes (open/closed) and trigger a warning for drowsiness.

This Python project enables hands-free control of your mouse using eye and face landmark detection via MediaPipe and OpenCV. It tracks facial features in real-time to move the mouse cursor with your eye movements and simulates a mouse click when you blink.