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Facial Recognition πŸ§” πŸ”

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 πŸ¦„

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

pip install requirements.txt

Description πŸ•΅οΈβ€β™‚οΈ

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 πŸ‘¨β€πŸ”¬

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

If you face any problem, kindly raise an issue

File Organization πŸ—„οΈ

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

Setup πŸ–₯️

  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 πŸ‰

python3 rec-feat.py

Results πŸ“Š

References πŸ”±

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A simple implementation of facial recognition using facenets for humans πŸ§” πŸ”

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