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A simple implementation of facial recognition using facenets for humans 🧔 🔍
| Date | Stars |
|---|---|
| 2026-07-24 | 290 |
| 2026-07-25 | 290 |
| 2026-07-28 | 290 |
| 2026-07-30 | 290 |
| 2026-08-06 | 290 |
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# Facial Recognition 🧔 🔍
[](https://github.com/akshaybahadur21/Facial-Recognition-using-Facenet/blob/master/LICENSE.txt) [](https://akshaybahadur.com)
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 🗄️
```shell
├── 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 📊
<img src="https://github.com/akshaybahadur21/BLOB/blob/master/Face-Rec.gif">
###### Made with ❤️ and 🦙 by Akshay Bahadur
## 📌 Cite Us
To cite this guide, use the below format:
```
@article{Facial-Recognition-using-Facenet,
author = {Bahadur, Akshay},
journal = {https://github.com/akshaybahadur21/Facial-Recognition-using-Facenet},
month = {01},
title = {{Facial-Recognition-using-Facenet}},
year = {2018}
}
```
## References 🔱
- Florian Schroff, Dmitry Kalenichenko, James Philbin (2015). [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/pdf/1503.03832.pdf)
- Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf (2014). [DeepFace: Closing the gap to human-level performance in face verification](https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf)
- 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
Excerpt of 4,005 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:590790ef2fb62913, topic:computer-vision, readme:face recognition
matched fp:590790ef2fb62913, topic:deep-learning