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, better detection and nice features.

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

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.

1. Detecting face only when your eyes are opened. (Security measure)
2. Using face align functionality from dlib to predict effectively while live streaming.
3. Unlock a MacOS using a given password when a specific user is identified in front of the camera

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

If you face any problem, kindly raise an issue

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.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 take new pictures using web cam. For doing that, run create-face.py the images get stored in /incept folder. You have to manually copy and paste them in the /images folder
3. Run rec-feat.py for running the application.

• 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