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https://github.com/naemazam/real-time-face-recognition-using-facenet
Real time face recognition Using Facenet , pytorch, Tensorflow
https://github.com/naemazam/real-time-face-recognition-using-facenet
facenet facenet-model facenet-pytorch facenet-trained-models mtcnn-face-detection naemazam python3 pytourch tensorflow tensorflow2
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Real time face recognition Using Facenet , pytorch, Tensorflow
- Host: GitHub
- URL: https://github.com/naemazam/real-time-face-recognition-using-facenet
- Owner: naemazam
- License: mit
- Created: 2022-06-03T06:38:04.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-06-20T09:47:52.000Z (over 2 years ago)
- Last Synced: 2023-03-05T13:58:02.353Z (over 1 year ago)
- Topics: facenet, facenet-model, facenet-pytorch, facenet-trained-models, mtcnn-face-detection, naemazam, python3, pytourch, tensorflow, tensorflow2
- Language: Python
- Homepage:
- Size: 12.8 MB
- Stars: 17
- Watchers: 2
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Real time face recognition Using Facenet 🧔 🤖 🔍
[](https://naemazam.github.io/Researcher/)
## 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. 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
3) Constant Face Location and Acknowledgment - Naem Azam
If you face any problem, kindly raise an issue
## File Organization 🗄️
```shell
├── Real-time-face-recognition-Using-Facenet (Current Directory)
├── encodings
├── architecture.py
├── detect.py
├── facenet_keras_weights.h5
├── train_v2.py
├── requirements.txt
├── Faces
├── Azam
└── winnie
└── JackieChan
└── readme.md
```
# Dependencies 💾
This code was working properly on tensroflow 2.3.0.
- Tensorflow 2.X
- numpy
- opencv-python
- mtcnn
- scikit-learn
- scipy
## Code Requirements 🦄
You can install Conda for python which resolves all the dependencies for machine learning.
`pip install requirements.txt`
## Menual dependencies install with pip 👨🔬
Install python 3.x and Conda
### [ Installing virtualenv](http://timsherratt.org/digital-heritage-handbook/docs/python-pip-virtualenv/)
`pip install virtualenv`
### [Install TensorFlow in windows ](https://www.tensorflow.org/install/pip#windows)
` conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 `
`python3 -m pip install tensorflow`
Verify install:
`python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"`
### [Install TensorFlow in Linux ](https://www.tensorflow.org/install/pip#windows)
` conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 `
`export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/`
`python3 -m pip install tensorflow`
Verify install:
`python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"`
### [install opencv-python 4.5.5.64](https://pypi.org/project/opencv-python/)
` pip install opencv-python `
### [install scikit-learn]()
` python -m venv sklearn-venv `
`sklearn-venv\Scripts\activate `
`pip install -U scikit-learn `
### [install mtcnn 0.1.1](https://pypi.org/project/mtcnn/)
` pip install mtcnn `
### [scipy](https://scipy.org/install/)
` python -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose `
## SetUp 🖥️
0. Download [facenet_keras_weights.h5](https://github.com/D2KLab/FaceRec/blob/master/model/facenet_keras_weights.h5) and put it accoding to our file Organization
1. Make a directory of your name inside the **Faces** folder and upload your 2-3 pictures of you.
2. Train Your System
```Python
python train_v2.py
```
## Real time face recognition 🧔 🤖 🔍
Run this for real time Face recognition, it will open your camera and start detection
```Python
python detect.py
```
## Results 📊
## Thesis 📰
Constant Face Location and Acknowledgment
By Naem Azam
DOI:10.13140/RG.2.2.35497.2672
## 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