# Real-Time Face, Age, Gender, and Emotion Detection System

## Project Objective
The objective of this project was to develop a real-time detection system that identifies faces, predicts gender, estimates age, and analyzes emotions in video feeds. The project aimed to provide valuable insights into demographics and emotional responses, useful in fields such as retail, market analysis, and user experience research.

## Technologies Used
- **Programming Language**: Python
- **Libraries & Frameworks**: OpenCV (for computer vision tasks), Caffe (for age and gender classification models), and Keras (for loading the pre-trained emotion detection model)
- **Environment**: Developed and tested on a local machine with webcam-based video feeds

## Role and Contributions
I served as the primary developer and integrator on this project. My responsibilities included:
- Setting up and configuring the detection pipeline using OpenCV's DNN module for face detection and pre-trained Caffe models for age and gender classification
- Integrating a Keras-based deep learning model for emotion detection and ensuring smooth processing of video feeds
- Designing utility functions to draw labels on frames and structuring the overall system for real-time performance
- Testing the system in various conditions to evaluate accuracy and reliability in real-time video feeds

## Final Outcomes
The project successfully delivered a prototype with the following features:
- **Real-Time Detection**: The system accurately detects faces, predicts gender and age, and analyzes emotions in real-time.
- **Detailed Demographic Insights**: Provides useful data on demographics and emotional responses, beneficial for applications in market research and user experience studies.
- **Performance**: Achieved reliable real-time performance on a standard local machine with a high level of accuracy for each detection task.

## Key Takeaways
This project enhanced my skills in computer vision and real-time data processing, particularly in integrating multiple pre-trained models into a cohesive system. It deepened my understanding of how AI solutions can be applied in practical settings to gather actionable insights from real-time data streams.

[GitHub Repository for Real-Time-Face-Age-Gender-and-Emotion-Detection-System](https://github.com/mrw-soumik/Real-Time-Face-Age-Gender-and-Emotion-Detection-System)