Python Face Recognition

Python Face Recognition is a robust and versatile tool that leverages cutting-edge computer vision techniques to detect, recognize, and analyze faces in images or video streams. This project incorporates powerful libraries like OpenCV, dlib, and face_recognition to deliver functionalities ranging from basic face detection to attendance tracking using face recognition.

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Features

Core Functionalities:

1. Face Detection: Accurately locate faces within images or video frames.
2. Face Recognition: Match detected faces with known individuals and label them accordingly.
3. Attendance System: Automatically mark attendance based on recognized faces and log timestamps.
4. Facial Encoding: Generate and store unique encodings for faces for efficient recognition.

Additional Features:

• Real-time face tracking and analysis via webcam or video input.
• Display recognition accuracy as a percentage for better interpretability.
• Easy integration with external tools or applications through generated CSV attendance logs.

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Installation

Prerequisites

Ensure Python 3.x is installed on your system. Additionally, install the following dependencies:

pip install opencv-python opencv-python-headless dlib face_recognition numpy

##Steps to Get Started

1. Clone this repository or download the project files:

git clone https://github.com/your-repo/python-face-recognition.git
cd python-face-recognition

2. Organize your known face images:

   • Place images in the Imageattend folder.
   • Ensure image filenames correspond to the person's name (e.g., elon.jpg).

3. Run the respective Python script based on your use case:

   • Basic Face Comparison:

   python basic.py

   • Real-Time Attendance System:

   python facerecog.py

Usage

Basic Face Comparison

• Compare two images (Images/elon musk.jpg and Images/elon test.jpg) to determine if they match.
• Displays the results on-screen, including match confidence.

Real-Time Attendance System

• Capture video from your webcam and recognize known faces.
• Logs recognized individuals' names and timestamps in Attendance.csv.

Code Overview

basic.py

• Loads two images, detects faces, and computes facial encodings.
• Compares encodings to identify matches and calculates confidence levels.
• Displays the results with bounding boxes around detected faces.

facerecog.py

• Preprocesses images in the Imageattend directory to compute known face encodings.
• Captures video, detects faces in real time, and compares them to the known encodings.
• Marks attendance for recognized faces in Attendance.csv, logging the name and timestamp.

Customization

Parameters

• Tolerance for Face Matching: Adjust the tolerance value in facerecog.py for stricter or more lenient matching.
• Image Resize: Modify the scale in imgSmall for faster processing.

Extensions

• Integrate with cloud storage or databases for centralized attendance management.
• Add support for emotion recognition or age estimation using additional models.

Contributing

Contributions are welcome! You can:

• Submit bug reports or feature requests via GitHub Issues.
• Create pull requests for enhancements or fixes.

License

This project is open-source and available under the MIT License.