anilknayak / FaceRecognition

Thesis Research and Implementation

Welcome to Face Recognition Using Moving Accumulator with various Deep Learning Architectures

Required Python APIs to run the system

1. pyqtgraph
2. numpy
3. scikit-learn
4. keras
5. tensorflow
6. json
7. imutils
8. opencv3
9. tqdm
10. protobuf
11. dlib
12. matplotlib

Below are the repo for some of the APIs

• conda install -c conda-forge keras
• conda install -c menpo opencv
• conda install -c conda-forge pyqtgraph
• conda install -c conda-forge tqdm
• pip install tensorflow
• conda install -c anaconda protobuf
• conda install -c stuwilkins protobuf
• conda install -c conda-forge matplotlib
• conda install -c anaconda keras-gpu
• conda install -c mlgill imutils
• conda install -c anaconda pyzmq
• conda install -c conda-forge typing
• conda install -c conda-forge decorator
• conda install -c conda-forge pytables
• conda install -c conda-forge packaging
• conda install -c conda-forge jupyterlab_launcher
• conda install -c conda-forge dlib=19.4
• conda install -c conda-forge pillow
• conda install -c numba numba

Language

Python 3.6

Setup

1. Clone this repository
2. FaceNet model : FaceNet Pre trained Model Download facenet pre trained model from above Google Drive Path and put it inside below folders

• facenet/model/
• trained_model/facenet/

3. You are all set

System Architecture Diagram

1. GUI Architecture

2. Training Architecture

Run

1. For GUI application run app.py
2. for training run train.py with your training instructions
3. To update configuration file as per you training instruction go to Configure Face Recognition - Not Ready