Face Recognition System with Food Preference Prediction

This project captures student photos via webcam, stores them in an SQLite database, and uses a neural network model to recognize faces and predict food preferences (veg/non-veg) in real-time. It leverages OpenCV for face detection and TensorFlow for prediction. The user interface is built using Streamlit.

Features

• Register New Student: Capture student photos and save them along with roll number, name, and food preference to an SQLite database.

• Train Model: Load student data from the database and train a neural network model to predict food preferences.

• Recognize and Predict: Capture a photo in real-time, recognize the student's face, and predict their food preference.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/face-recognition-food-pref.git

   cd face-recognition-food-pref

2. Install the required packages:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

Usage

1. Register a new student:

   o Enter the roll number, name, and food preference (veg/non-veg).

   o Capture the student's photo using the webcam.

   o The captured photo and student details are stored in the database.

2. Train the model:

   o Click the "Train Model" button to train the neural network model using the stored student data.

   o The trained model is saved to disk.

3. Recognize and predict food preference:

   o Click the "Start Recognition" button to capture a photo in real-time.

   o The system detects the face, recognizes the student, and predicts their food preference.

   o The predicted food preference is displayed along with the captured photo.

Technologies Used

• Python: Programming language used for implementation.

• Streamlit: Framework for creating the web-based user interface.

• OpenCV: Library for real-time computer vision tasks, including face detection and image processing.

• TensorFlow: Library for training and deploying the neural network model.

• SQLite: Database for storing student information and photos.

Project Structure

• app.py: Main application script.

• requirements.txt: List of required packages.

• food_pref_model.h5: Trained neural network model for food preference prediction (generated after training)

Note

• Ensure that your webcam is connected and functional.

• The project requires cv2, numpy, tensorflow, streamlit, and PIL libraries to be installed.

• The model is trained using images of size 480x640. Adjust the image size in the code if needed.