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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.
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This project is a real-time drowsiness detection system that monitors eye activity using a webcam and alerts the user when signs of fatigue are detected. It enhances safety in critical situations such as driving or operating machinery.
✅ Real-time face and eye detection
✅ Eye blink and drowsiness analysis using facial landmarks
✅ Visual feedback: "Active," "Drowsy," or "Sleeping" state
✅ Buzzer alert if the user is detected as "Sleeping"
📸 Face Detection: Detects the user's face using Dlib’s pre-trained model.
👀 Eye Aspect Ratio (EAR): Measures eye openness using key facial landmarks.
⚡ State Classification:
1️⃣ Install Dependencies:
pip install opencv-python numpy dlib imutils playsound pillow
2️⃣ Download Pre-Trained Model:
shape_predictor_68_face_landmarks.dat from Dlib’s GitHub and place it in the project directory.3️⃣ Run the Project:
python drowsiness_detection.py
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harshit-saraswat /
This is a small fun project which uses face recognition techniques to separate images from a large dataset into images of different people according to faces.
37/100 healthutkarsha-ecsion /
# Attendance-System-Face-Recognition This project is a web application demonstrating the use of facial recognition for marking attendance built as a part of my PS -1. It is a web application that can be used by the company to manage attendance of its employees. ## Functionality Supported - Admin and Employee Login - Admin : Register new employees. - Admin : Add employee photos to the training dataset. - Admin: Train the model. - Admin: View attendance reports of all employees. Attendance can be filtered by date or employee. - Employee - View attendance reports of self. ## Built Using - **OpenCV** - Open Source Computer Vision and Machine Learning software library - **Dlib** - C++ Library containing Machine Learning Algorithms - **face_recognition** by Adam Geitgey - **Django**- Python framework for web development. ### Face Detection - Dlib's HOG facial detector. ### Facial Landmark Detection - Dlib's 68 point shape predictor ### Extraction of Facial Embeddings - face_recognition by Adam Geitgey ### Classification of Unknown Embedding - using a Linear SVM (scikit-learn) The application was tested on data from 9 employees.
1️⃣ Open the application.
2️⃣ The webcam feed will display in the GUI.
3️⃣ The system monitors eye activity and updates the state: "Active," "Drowsy," or "Sleeping".
4️⃣ If "Sleeping" is detected for over 7 seconds, an alert will sound.
Drowsiness Detection/
│
├── drowsiness_detection.py # Main script
├── buzzer.mp3 # Audio alert file
├── shape_predictor_68_face_landmarks.dat # Pre-trained model
└── README.md # Project documentation
🔹 Driver Safety: Prevent drowsy driving accidents.
🔹 Workplace Monitoring: Improve worker alertness in critical environments.
🔹 Personal Alertness: Help individuals stay focused during long tasks.
🔹 Yawning detection for better accuracy.
🔹 Deep learning models for advanced drowsiness prediction.
🔹 Mobile-friendly version of the application.
🔹 Multilingual support for international users.
This project is available for personal or educational use. Feel free to customize or extend it! 🚀
🔥 Stay Alert, Stay Safe! 🚀
mohammed-Emad /
The purpose of this project is to implement the Tensorflow and Dlib libraries in Python along side some computer vision techniques to create a human interface device that replaces the functionality of a mouse using a webcam, face tracking, and separate convolutional neural networks to detect specific changes in facial expression. More specifically we aim to create a program that detects when a user's head is looking in a certain direction and then move the mouse cursor in the corresponding direction regardless of the webcams orientation relative to the user. More so, we aim to allow the user to make left and right mouse clicks by closing either eye such that when the user closes an eye shut the corresponding click down event is triggered, and then after when the user opens the same eye the corresponding click up event is triggered. The end result is a series of models that run in real time to allow the user smooth control of mouse movement and clicks. Further development of the methods and techniques used in this project could one day be used to allow individuals with certain physical disabilities to better interact with their computer devices.
30/100 healthSaurabh7Goku /
This is a minor project of Eye Blink detection where we used Python as language and tools like OpenCV, Dlib and some computer vision concepts
28/100 healthmayanksinghrathore /
This computer vision project uses opencv, python,face-recognition, cmaker, and dlib packages to complete. It is capable of real-time video capture that it uses to match photos. As the match is completed it gives registers the name and time in a csv file. First photos are converted from RGB to BGR.
31/100 healthcemdurakk /
A real-time driver fatigue detection system using computer vision. This project monitors eye movements to detect drowsiness and alerts the driver with a visual and audible warning when fatigue is detected. Built with OpenCV, dlib, and Python, it's designed for enhancing driver safety.
38/100 health