Machine Learning Driven Identity Verification

This repo contains a Jupyter Notebook that utilizes a Tensorflow Model to identity and smartly crop an Identity Card, along with an accompanying Flask app. The model was trained using a specially formatted version of the the MIDV-500 dataset where the images were converted from TIF to JPG and the extraneous metadata in the annotations removed. This model also works for isolating and cropping photos of paper receipts and possibly any conceivable rectangular images. To learn how to train the model see repo KYC-train-model.

Installation Instructions

Jupyter Notebook

1. Create and activate a Python 3 Virtual environment.

python3 -m venv env

source env/bin/activate

2. Install Requirements.

pip install -r requirements.txt

3. Start Jupyter Notebook.

ipython notebook --ip=127.0.0.1

4. Open & Run Tensorflow - Verification.ipynb

The mechanics of the Tensorflow model and OpenCV transforms is documented inline.

Flask App

This demonstrates uploading the identity document via a Flask app with the relevant pre-processing OpenCV steps. The machine learning model is optimized for images that are acquired via an Android or Samsung Phone. Use ngrok to share localhost URL for mobile browser testing.

Notes:

The Step 1 Form and Step 3 selfie or not fully functioning yet.

You must take a photo of ID card in Step 2 and selfie in Step 3 or app will break.

App.py behaves very similarily to the Jupyter Notebook , with a few exceptions documented as comments in the code.

1. Create and activate a Python 3 Virtual environment.

python3 -m venv env

source env/bin/activate

2. Install Requirements.

pip install -r requirements.txt

3. Start Flask app.

python app.py

4. In a separate terminal tab start ngrok.

./ngrok http 5000

5. Test ngrok URL on mobile browser. Final ID image is saved in tmp/FINAL.jpg.

#TODO

1. Connect Step 1 form to database
2. OCR ID Card and save JSON results to database
3. Compare ID Card image with selfie and save JSON results to database
4. Compare form submission and OCR results.

Citation

Please cite this paper, if using midv dataset, link for dataset provided in paper

@article{DBLP:journals/corr/abs-1807-05786,
  author    = {Vladimir V. Arlazarov and
               Konstantin Bulatov and
               Timofey S. Chernov and
               Vladimir L. Arlazarov},
  title     = {{MIDV-500:} {A} Dataset for Identity Documents Analysis and Recognition
               on Mobile Devices in Video Stream},
  journal   = {CoRR},
  volume    = {abs/1807.05786},
  year      = {2018},
  url       = {http://arxiv.org/abs/1807.05786},
  archivePrefix = {arXiv},
  eprint    = {1807.05786},
  timestamp = {Mon, 13 Aug 2018 16:46:35 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1807-05786},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}