(Research) AI Blink Detection and Reminder (1) – Project Introduction

[Open Source + Research Paper + Jetson Nano] Blinker Project

This project aims to implement a blink detection system using dlib and OpenCV, following an existing open-source implementation. It builds upon facial recognition techniques to develop a system capable of detecting eye blinks in real-time.

According to the contributor's description, this system can be applied in various scenarios, such as:
🚗 Drowsy driving detection – Alerting drivers when signs of fatigue are detected.
📚 Student monitoring – Analyzing drowsiness and focus levels in study environments.

By leveraging Jetson Nano, this project explores the integration of edge AI for real-time blink detection, opening possibilities for applications in safety, education, and human-computer interaction. 🚀

asily Implementable with a Camera and Software Development Setup

If you have a camera and a properly configured software development environment, this project is relatively easy to implement.

In this project, we will maximize the use of open-source resources by following these key steps:

1️⃣ Understanding the key libraries and source code used in blink detection.
2️⃣ Setting up the required development environment to run the project smoothly.
3️⃣ Optimizing performance for real-time blink detection and efficiency.
4️⃣ Sharing results and discussing improvements to enhance functionality.
5️⃣ Going beyond simple code execution to explore potential service applications.

To start, we will download high-performance open-source projects from GitHub and install them in our own development environments.

⚠️ Note: Simply running pip install ... without careful preparation may lead to a series of error messages that could test your patience. Make sure to read through the instructions carefully and follow each step methodically for a smooth setup. 🚀


Project Goal: Blink Detection

Contributor (Author): Adrit Rao
Research Paper: Blink Detection Research

This project focuses on implementing a blink detection system, utilizing dlib and OpenCV for real-time eye-tracking and analysis. 🚀

Blinkr: AI-Powered Blink Detection System

🔹 Overview

AI-powered blink detection system
Counts user blinks and issues a warning if blinking is insufficient

🛠️ System Setup

1️⃣ Service Environment

📌 Hardware Requirements

Front-facing camera for real-time eye tracking
Blinkr device powered by NVIDIA Jetson Nano AI Computer
Jetson Nano: A high-performance single-board computer designed for AI applications
Runs on a computer with a camera, speaker, and display

2️⃣ Development Environment

💻 Source Code: GitHub - Blinkr
📂 Model File (Shape Predictor): 68 Face Landmarks Predictor

📦 Required Libraries

OpenCV (opencv)
dlib (dlib)

▶️ Execution Results

Upon running the system:
✅ Real-time blink detection is performed using OpenCV & dlib
✅ Blink count is tracked dynamically
✅ If blinking is insufficient, the system alerts the user

This project effectively leverages AI-powered facial tracking to provide real-time feedback on blinking habits, making it ideal for drowsiness detection and focus monitoring applications. 🚀



Blinked 1
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📌 Required Libraries & Installation Guide

⚠️ Important Notes:

Run all installation commands as an administrator (for Windows, exclude sudo).
Ensure the correct file paths are set for necessary dependencies.

1️⃣ [DAT File] – Face Landmark Predictor

📌 File: "shape_predictor_68_face_landmarks.dat"

🔹 Download & Path Configuration

Even if dlib is installed, this file might not be included.
Manually download the file and place it in the working directory.

📍 Path Setting Example in Python

python
import dlib

# Set the correct path for the landmark predictor
datFile = "./shape_predictor_68_face_landmarks.dat"

# Load the face landmark predictor
dlib_facelandmark = dlib.shape_predictor(datFile)

2️⃣ OpenCV Installation

📌 OpenCV Overview:
OpenCV does not officially support pip installation, but there are third-party repositories that make it possible.

📌 Required Packages:

filterpy (for Kalman filters and sensor fusion)
opencv-contrib-python (includes extra OpenCV modules)
opencv-python (core OpenCV library)

🔹 Install Commands (Run as Administrator)

bash
pip install filterpy
pip install opencv-contrib-python
pip install opencv-python

📌 Ensure you have administrator privileges before running these commands!

    • [dlib] (C++ Library)

      🔹 Description

      • Built on deep learning, specifically designed for facial recognition applications.
      • Achieves 99.38% accuracy on the Labeled Faces in the Wild (LFW) dataset.
      • Provides a simple face recognition tool via CLI, allowing face detection within an image folder.

      🔗 Download: http://dlib.net/

                                 

Installing dlib in an Anaconda Environment

For users running Anaconda, access the downloaded folder through Anaconda 3 Powershell Prompt and follow the installation steps.

🔹 Installation Process (Approx. 10 minutes)

Navigate to the downloaded dlib folder and run the following command:

bash
python setup.py build

⚠️ Important Notes

1️⃣ Path Adjustments Required

Many open-source scripts include predefined file paths that may not match your local setup.
Manually update paths in each script to match the actual directory structure.

2️⃣ Example Path Modifications
📌 Original:

python
xxx_train("knn_examples/train")

📌 Modified:

python
xxx_train("./examples/knn_examples/train")

Ensure that all file paths are correctly updated to prevent errors when running the scripts. 🚀


      •  
    • python setup.py install

    Installation Verification

    To confirm that dlib has been successfully installed, run the following commands in Python:

    python
    >>> import dlib  # If no errors occur, the installation was successful
>>> dlib.__version__  # Check the installed dlib version

    Source Code Structure

    🔹 Key Components & Features

    Facial recognition
    Face landmark detection
    Machine learning-based classification

    Development Environment

    📌 OS: Windows 10
    📌 IDE: VS Code
    📌 Python Version: Python 3.3+ or Python 2.7

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