Face Detection WebApp

Overview

Developed a web-based face detection application that utilizes computer vision techniques to detect and analyze human faces in real-time video streams or uploaded images. The application provides detailed facial analysis including emotion detection, age estimation, and gender recognition.

Key Features

• Real-time Face Detection: Process webcam feeds for immediate face detection
• Image Upload Analysis: Analyze faces in uploaded photos
• Multiple Face Processing: Detect and analyze multiple faces simultaneously
• Facial Feature Analysis: Identify key facial landmarks and features
• Emotion Detection: Recognize emotional expressions in detected faces
• Age and Gender Estimation: Provide demographic insights about detected individuals
• User-Friendly Interface: Intuitive web interface for easy interaction

Technical Implementation

• Implemented face detection algorithms using OpenCV and deep learning models
• Developed a responsive Flask web application for the user interface
• Created efficient image processing pipelines for both static images and video streams
• Optimized the application for performance on standard hardware
• Ensured privacy by processing all data locally without external storage

Technologies Used

• Python
• OpenCV
• Flask
• TensorFlow
• HTML/CSS/JavaScript
• Docker (for deployment)

GitHub Repository

View the source code and documentation on GitHub.

Project Timeline

January 2025 - February 2025

Applications

This application can be utilized for:

• Security and access control systems
• User experience research
• Marketing analytics
• Educational demonstrations
• Personal projects and entertainment

Future Enhancements

• Facial recognition capabilities
• Enhanced emotion detection accuracy
• Mobile application version
• Extended demographic analysis
• Batch processing for large image sets

Contact

For a demonstration or to discuss potential applications, please contact me.