Authorization System

Person identification based on scan of Polish document and recorded images by camera. David's Sandberg facenet implementation was used for face recognition.

The code was tested on Ubuntu 18.04 LTS with HP 3070A/Fujitsu fi-65F (scanners with 600 DPI) and front camera in DELL XPS/Intel RealSense Depth Camera D435.

Dependencies

First of all, it is needed to install Tesseract. It's installation tutorial can be found here. For Polish letters remember to install tesseract-ocr-pol. We used tesseract 4.0.0-beta.1 version.

Some packages are required before installation our library:

numpy==1.15.1
scipy==1.1.0
imageio==2.4.1
opencv-python==3.4.2.17
pytesseract==0.2.4
pyinsane2==2.0.13
pyrealsense2==2.14.1.91
tensorflow==1.7.0
facenet==1.0.3

You can install it with pip install -r requirements.txt. If you have GPU-enabled PC you can use tensorflow-gpu==1.8.0 or greater.

We recommend to use virtualenv with Python 3.6.X (3.6.5 or greater).

Required FaceNet model can be downloaded from here. This model was trained on VGGFace2 dataset.

Installation

You can install authorization system library in two ways.

Using wheel file:

pip install authorization_system-*.whl

or being within folder with setup.py:

pip install -e .

Usage

To launch authorization system use:

python path_to_folder_containing_file/authorization.py [args]

To see all options for authorization.py use:

python path_to_folder_containing_file/authorization.py --help

Examples:

python ./authorization.py

python ./authorization.py --facenet_model_checkpoint_dir ../models/facenet/ --document_type_detector_means_path ../models/document_type_detector_means.pkl

python ./authorization.py --facenet_model_checkpoint_dir ../models/facenet/ --document_type_detector_means_path ../models/document_type_detector_means.pkl --save_data --data_save_dir ../scanned_documents/ --realsense --video_output_scale 1 --video_width 1280 --video_height 720

To quit press Ctrl + C in the console.

Data

Attached data contains examples of two scanned documents and templates for document type detector. Data is enscrypted and a password is IA_edoctfaD (written backwards).

Remarks

Every scanner device is different, so don't worry if something go wrong on the first try. Correct working of our solution depends on a proper cutting of the document from scanned image. Therefore, it is important to set a good position of the document's area. The area of the cut or final image should have 1180x1960px.

If you want to have solution insensitive to document flipping, use any graphics software to make sure that you get similar images after cutting off and flipping (with very small shifts).

Due to the quality of different scanners, you can adjust gamma of the scans. It can be helpful, if there is any problem with document's type detection.

The resolution of video captured by the camera is set up to 640x480px and scale equaled to 2 by default. If you use Intel RealSense Depth Camera D435 you should set up it to 1280x720px with scale equaled to 1. Remember to set scale correctly, because this parameter is also used for displaying bounding-boxes.

Fraud detection is sensitive to the face's position. If you try to "hide" face, it should be detected as fraud. Therefore, keep face straight to the camera.

Authors

Piotr Smuda
Piotr Tempczyk

dantodor / authorization-system Goto Github PK