FaceRecognition-MTCNN-ArcFace

FaceRecognition with MTCNN using ArcFace

🚀 New Update (27-01-2023)

• Liveness Model:

  • Liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems
  • Our FaceRecognition system initially will check the faces are Fake or NOT
  • If its a Fake face it will give warnings
  • Otherwise it will go for Face-Recognition

Clone this Repository

git clone https://github.com/naseemap47/FaceRecognition-MTCNN-ArcFace.git
cd FaceRecognition-MTCNN-ArcFace

Install dependency

pip3 install -r requirements.txt

Custom Face Recognition

You can use:

• Command Line
  

• Streamlit Dashboard

Streamlit Dashboard

⚠️ New version NOT Available, Not updated Liveness Model

RUN Streamlit

streamlit run app.py

Command Line (Recommended)

1.Collect Data using Web-cam or RTSP

Example:

python3 take_imgs.py --source 0 --name JoneSnow --save data --conf 0.8 --number 100

:book: Note:
Repeate this process for all people, that we need to detect on CCTV, Web-cam or in Video.
In side save Dir, contain folder with name of people. Inside that, it contain collected image data of respective people.
Structure of Save Dir:

├── data_dir
│   ├── person_1
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
│   ├── person_2
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
.   .
.   .

2.Normalize Collected Data

It will Normalize all data inside path to save Dir and save same as like Data Collected Dir

Example:

python3 norm_img.py --dataset data/ --save norm_data

Structure of Normalized Data Dir:

├── norm_dir
│   ├── person_1
│   │   ├── 1_norm.jpg
│   │   ├── 2_norm.jpg
│   │   ├── ...
│   ├── person_2
│   │   ├── 1_norm.jpg
│   │   ├── 2_norm.jpg
│   │   ├── ...
.   .
.   .

3.Train a Model using Normalized Data

Example:

python3 train.py --dataset norm_data/ --batch_size 16 --epochs 100

Inference

On Image

Example:

python3 inference_img.py --source test/image.jpg --model models/model.h5 --conf 0.85 \
                     --liveness_model models/liveness.model --label_encoder models/le.pickle

To Exit Window - Press Q-Key

On Video or Webcam

Example:

# Video (mp4, avi ..)
python3 inference.py --source test/video.mp4 --model models/model.h5 --conf 0.85 \
                     --liveness_model models/liveness.model --label_encoder models/le.pickle

# Webcam
python3 inference.py --source 0 --model models/model.h5 --conf 0.85 \
                     --liveness_model models/liveness.model --label_encoder models/le.pickle

To Exit Window - Press Q-Key

🚀 Liveness Model

Liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems
If you wants to create a custom Liveness model, Follow the instruction below 👇:

Data Collection

Collect Positive and Negative data using data.py

Example:

cd Liveness
python3 data.py --source 0 --name positive  # for positive
python3 data.py --source 0 --name negative  # for negative

Train Liveness Model

Train Liveness model using collected positive and negative data

Example:

cd Liveness
python3 train.py --dataset data --batch_size 8 --epochs 50

Inference

Inference your Custom Liveness Model

cd Liveness
python3 inference.py --source 0 --conf 0.8