This project leverages dlib, OpenFace, and Keras to perform face identification and recognition. The model extracts numerical embeddings from faces and classifies them using a deep learning model. It is deployed using Flask for real-time predictions.
- Face Identification using the dlib library
- Face Embeddings generated using the OpenFace model
- Deep Learning-based Face Recognition using a Keras Sequential Model
- Web Deployment using Flask
After deploying the Flask application, the interface looks as follows:
Ensure you have the following dependencies installed:
pandasflaskkerasdlibpicklePILbase64ionumpyos
Follow these steps to run the project:
- Install Flask and add it to your environment path
- Clone the Repository
- Run the Flask Server
Open a terminal or command prompt in the project directory and execute:
flask run --host=0.0.0.0
- Open the Application in a Browser
- Navigate to the provided localhost URL
- Upload an image and predict!
This project demonstrates an end-to-end pipeline for face recognition, making it useful for security systems, biometric authentication, and other AI applications.
Let me know if you need any modifications! 🚀