FaceRecognition-MTCNN-ArcFace

FaceRecognition with MTCNN using ArcFace

Clone this Repository

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

Install dependency

pip3 install -r requirement.txt

Custom Face Recognition

You can use:
Command Line
OR
Streamlit Dashboard

Streamlit Dashboard

Install Streamlit

pip3 install streamlit

RUN Streamlit

streamlit run app.py

Command Line

1.Collect Data using Web-cam

python3 take_imgs.py --name <name of person> --save <path to save dir>

Example:

python3 take_imgs.py --name JoneSnow --save data

📖 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

python3 norm_img.py --dataset <path to collected data> --save <path to save 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

python3 train.py --dataset <path to normalized Data> --save <path to save model.h5>

Example:

python3 train.py --dataset norm_data/ --save model.h5

Inference

📖 Note:

Open inference_img.py and inference.py:-
Change class_names List into your class names. Don't forget to give in same order used for Training the Model.

On Image

python3 inference_img.py --image <path to image> --model <path to model.h5> --conf <min model prediction confidence>

Example:

python3 inference_img.py --image data/JoneSnow/54.jpg --model model.h5 --conf 0.85

To Exit Window - Press Q-Key

On Video or Webcam

python3 inference.py --source <path to video or webcam index> --model <path to model.h5> --conf <min prediction confi>

Example:

# Video (mp4, avi ..)
python3 inference.py --source test/video.mp4 --model model.h5 --conf 0.85

# Webcam
python3 inference.py --source 0 --model model.h5 --conf 0.85

To Exit Window - Press Q-Key