Abstract
Face recognition system using python leverages the OpenCV library integrated with streamlit for building an interactive, user-friendly face recognition platform.
- Face recognition is a rapidly evolving and crucial challenge in computer vision, with broad applications in security, education, and healthcare sectors.
- OpenCV is a versatile and advanced library for image processing, compatible with Python, C++, Java, and other languages.
- Streamlit enables fast prototyping and deployment of data-driven web interfaces in Python, ideal for AI application demos.
- In this project, the following key methodologies are implemented:
- Face Data Collection from Webcam using OpenCV
- Real-Time Face Detection using Haar Cascade classifier
- Storing facial images and user metadata for training dataset management
- Face Recognition using LBPH (Local Binary Patterns Histograms) algorithm
- Model Training, Prediction, and Confidence Threshold Handling
- User Database Management for adding/deleting identities via Admin Panel
- Streamlit-powered interface for seamless data collection, recognition, and administration
- The system workflow includes:
- Real-time face detection, capturing and saving grayscale facial images per user
- Training the LBPH model and persisting it to disk for future recognition
- Live recognition from webcam feed, labeling known faces based on trained model
- Allowing administrators to manage the user database and retrain as necessary
- The combination of OpenCV’s reliable feature extraction and streamlit’s rapid UI deployment creates a robust platform for practical face recognition tasks in academic and industrial settings.
Existing System
- Existing face recognition systems primarily focus on basic face detection and static recognition without flexible user interaction or dataset management.
- They often lack features for efficient, user-driven face data collection and dynamic model updating.
- Current implementations generally do not provide comprehensive options for managing the number of face samples per user or controlling the quality of images collected.
- Most systems do not include administrative tools for user database management, such as adding or deleting identities through a graphical interface.
- Real-time display of recognition results is common, but few systems allow live visualization of collected faces or confidence scores in an interactive manner.
- There is limited customization available for recognition thresholds or labelling accuracy control by end-users.
- Existing systems rarely output detailed arrays of face data points or recognition metadata for further analysis or user inspection.
Proposed System
- The proposed face recognition system provides a comprehensive, optimized pipeline for real-time face detection, dynamic dataset management, and live recognition with Streamlit’s interactive interface.
- Complete customization is available, including:
- User-controlled number of face images collected per individual.
- Live monitoring of face sample quality with automatic rejection of poor detections.
- Adjustable confidence thresholds for recognition labelling, improving reliability.
- The system enables live display of captured face images and recognition results with clear labelling and feedback.
- It consolidates face images and recognition metadata as accessible data arrays for easy management and potential further processing.
- Administrative capabilities allow managing the user database securely with password protection, including adding new users, deleting existing ones, and retraining the model seamlessly.
- Recognition outputs can be customized with user-preferred visual markers (e.g., bounding box colors and label formats) for better clarity during live recognition sessions.
- Overall, the system integrates real-time interactivity, customizable parameters, and robust user management unavailable in most existing implementations.
Hardware Requirements
- Processor : Intel Core i3 (or above)
- System Type : 64-bit Operating System
- Storage : 500 GB HDD / SSD
- RAM : 4 GB (minimum)
Software Requirements
- Operating System : Windows 10 (or above)
- Software : Anaconda Navigator, Python IDE (e.g., Jupyter Notebook or VS Code)
- Python Libraries : OpenCV
Architecture Diagram
Project Modules
- Webcam Capture & Face Detection Module
- Face Image Collection & Storage Module
- Model Training Module
- Face Recognition Module
- Administrative Controls Module
- Streamlit User Interface Module
Components of Project Report
- Abstract
- Table of Contents
- List of Tables
- List of Figures
- Chapters
- Introduction
- Literature review
- Problem definition and requirement analysis
- Design and Implementation
- Testing and deployment
- Future enhancements
- Summary
- References
Project Report Pages : 80
Can be used in : Python
Delivery Time : Within 2 hours.
Support / Query : Call +91-7449000533
Email [email protected]
