BMI-Detection-with-Facial-Features

This project uses a deep learning model to predict a person's BMI (Body Mass Index) and gender based on two input images: a front-view and a side-view image. The model leverages a pre-trained VGG16 network for feature extraction and has been fine-tuned for this specific task.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Output Results
• Model Details
• Limitations
• Contributing

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Overview

This project predicts:

1. BMI (Body Mass Index): A numerical value indicating whether a person is underweight, normal, or overweight.
2. Gender: Categorized as male or female based on the input images.

The model processes two images: a front view and a side view. Predictions are based on features extracted using a convolutional neural network.

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Features

• Two-Image Input: Utilizes front and side view images for accurate prediction.
• BMI Categorization: Categorizes BMI as:
  • Underweight
  • Normal
  • Overweight
• User-Friendly Interface: Supports selecting images using a graphical file dialog.
• Expandable: The model can be further trained or integrated into larger systems.

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Installation

Prerequisites

• Python 3.7 or later
• Required Python libraries:
  • TensorFlow
  • NumPy
  • Pillow
  • Tkinter (for GUI)

Steps

1. Clone this repository:

   git clone https://github.com/your-username/BMI-detection-with-facial-features.git
   cd BMI-detection-with-facial-features

2. Install dependencies:

   pip install -r requirements.txt

3. Download the trained model and save it to the appropriate path:

   C:\Users\azadk\OneDrive\Desktop\projects\bmi_detection\log\model_epoch_07_val_loss_29.54.keras
   

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Usage

1. Run the script:

   python bmi_gender_prediction.py

2. A dialog will prompt you to select two images:

   • Front View Image
   • Side View Image

3. The script will display:

   • Predicted BMI
   • BMI Category (Underweight, Normal, Overweight)
   • Predicted Gender (Male or Female)

4. Example Output:

   

   Predicted BMI: 23.45
   Predicted Gender: Male
   BMI Category: Over Weight
   

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Output Results

After running the script, the output is displayed in the terminal. Here’s a breakdown of what each output represents:

1. Predicted BMI:

   • A numerical value, such as 23.45, representing the predicted Body Mass Index.

2. BMI Category:

   • Describes the category based on the BMI:
     • Underweight: BMI < 18.5
     • Normal: 18.5 ≤ BMI < 25
     • Overweight: BMI ≥ 25

   

Model Details

• Architecture: Based on VGG16 for feature extraction.
• Input Size: 224x224 pixels for both front and side view images.
• Outputs:
  • BMI (continuous value)
  • Gender (binary classification: Male/Female)

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Limitations

• Image Quality: Low-resolution or poorly-lit images may affect predictions.
• Bias: The model's accuracy may vary based on the dataset it was trained on.
• Generalization: Predictions may not generalize well to certain populations or body types not represented in the training data.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Commit your changes:

   git commit -m "Description of changes"

4. Push to your branch and create a pull request.