Face Emotion & Age Detection System

Overview

This application detects faces in real-time using a webcam, recognizes known faces, estimates emotions and ages, and generates detailed reports (CSV and PDF) based on the detection logs. It uses advanced libraries like face_recognition, dlib, and OpenCV for face detection, emotion recognition, and age estimation.

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Features

1. Real-Time Face Detection:

   • Detects faces using Haar Cascade and face_recognition.
   • Draws bounding boxes around detected faces.

2. Emotion Recognition:

   • Recognizes emotions such as happy, sad, angry, neutral, etc., using facial landmarks.

3. Age Estimation:

   • Estimates the age of detected faces using a pre-trained Caffe model.

4. Known Face Recognition:

   • Recognizes known faces from the known_faces directory.
   • Automatically saves new faces and prompts for the user's name.

5. Log Generation:

   • Logs all detections (name, emotion, age) into a file (logs/detection_log.txt).

6. Report Generation:

   • Generates a CSV report summarizing emotions and ages for each user.
   • Generates a PDF report with a clean and professional design.

7. Error Handling:

   • Handles cases where face encoding fails or log files are missing.

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Installation

Prerequisites

• Python 3.8 or higher
• A webcam for real-time detection

Dependencies

Install the required libraries using the following command:

pip install opencv-python dlib face_recognition pandas fpdf

Pre-trained Models

Download the following pre-trained models and place them in the project directory:

1. Shape Predictor Model: shape_predictor_68_face_landmarks.dat
2. Age Estimation Model:
   • deploy_age.prototxt
   • age_net.caffemodel

Place these files in the root directory of the project.

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Directory Structure

The project has the following structure:

project/
│
├── known_faces/       # Directory to store known face images
│   ├── Alice/
│   │   ├── Alice_20231005_143005.jpg
│   ├── Bob/
│   │   ├── Bob_20231005_143200.jpg
│
├── logs/              # Directory to store logs and reports
│   ├── detection_log.txt
│   ├── report.csv
│   ├── report.pdf
│
├── shape_predictor_68_face_landmarks.dat
├── deploy_age.prototxt
├── age_net.caffemodel
├── app.py             # Main script
└── README.md          # This file

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Usage

Running the Application

1. Start the application by running the following command:

   python app.py

2. The webcam will open, and the system will start detecting faces in real-time.

3. Key Commands:

   • Press q to quit the application.
   • Press r to generate a CSV and PDF report.

Adding Known Faces

• Place images of known faces in the known_faces directory under subfolders named after the person (e.g., known_faces/Alice/).
• Restart the application to load the new faces.

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Report Generation

When you press r, the system generates two reports:

1. CSV Report:

   • File: logs/report.csv
   • Contains details about detected emotions, counts, average age, and all recorded ages for each user.

2. PDF Report:

   • File: logs/report.pdf
   • A visually appealing summary of emotions and ages for each user.

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Example Log Entry

The log file (logs/detection_log.txt) contains entries like this:

2023-10-05 14:30:00 - Name: Alice, Emotion: happy, Age: (25-32) (Est: 28)
2023-10-05 14:30:01 - Name: Bob, Emotion: angry, Age: (38-43) (Est: 40)

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Troubleshooting

1. Missing Log File

If the log file (logs/detection_log.txt) is missing:

• Ensure the logs directory exists. If not, create it manually:

  mkdir logs

2. No Faces Detected

• Ensure proper lighting and that the face is within the camera's view.
• Adjust the scaleFactor and minNeighbors parameters in the detectMultiScale function if necessary.

3. Errors During Installation

• If you encounter issues installing dlib, ensure you have the required build tools installed:
  • On Windows: Install CMake and Visual Studio Build Tools.
  • On Linux: Install cmake and build-essential.

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Contributing

Contributions are welcome! If you find any bugs or want to add new features, feel free to submit a pull request.