Facial Recognition 🧔 🔍

This code helps in facial recognition using facenets (https://arxiv.org/pdf/1503.03832.pdf). The concept of facenets was originally presented in a research paper. The main concepts talked about triplet loss function to compare images of different person. This concept uses inception network which has been taken from source and fr_utils.py is taken from deeplearning.ai for reference. I have added several functionalities of my own for providing stability and better detection.

Code Requirements 🦄

You can install Conda for python which resolves all the dependencies for machine learning.

pip install requirements.txt

Description 🕵️‍♂️

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiples methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database.

Functionalities added 🧟

1. Detecting face only when your eyes are opened. (Security measure)
2. Using face align functionality from dlib to predict effectively while live streaming.

Python Implementation 👨‍🔬

1. Network Used- Inception Network
2. Original Paper - Facenet by Google

If you face any problem, kindly raise an issue

File Organization 🗄️

├── Facial-Recognition-using-Facenet (Current Directory)
    ├── models : Saved Models
        ├── face-rec_Google.h5 : Facenet Model 
        └── shape_predictor_68_face_landmarks.dat : Facial Keypoints Model
    ├── utils : Utils Folder
        ├── fr_utils.py 
        └── inception_blocks_v2.py 
    ├── create_face.py : Store the faces for module
    ├── rec-feat.py - Main Application
    ├── Train-inception.py : Model Trainer
    ├── LICENSE
    ├── requirements.txt
    └── readme.md
        

Setup 🖥️

1. If you want to train the network , run Train-inception.py, however you don't need to do that since I have already trained the model and saved it as face-rec_Google.h5 file which gets loaded at runtime.
2. Now you need to have images in your database. The code check /images folder for that. You can either paste your pictures there or you can click it using web cam. For doing that, run create-face.py the images get stored in /incept folder. You have to manually paste them in /images folder
3. Run rec-feat.py for running the application.

Execution 🐉

python3 rec-feat.py

Results 📊

Made with ❤️ and 🦙 by Akshay Bahadur

References 🔱

• Florian Schroff, Dmitry Kalenichenko, James Philbin (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering
• Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf (2014). DeepFace: Closing the gap to human-level performance in face verification
• The pretrained model we use is inspired by Victor Sy Wang's implementation and was loaded using his code: https://github.com/iwantooxxoox/Keras-OpenFace.
• Our implementation also took a lot of inspiration from the official FaceNet github repository: https://github.com/davidsandberg/facenet