Fingerprint matcher

Python 3.6+ implementation of a fingerprint matching minutiae-based model.

Project status: In progress

Components status:

Data

Data processing: DONE Minutiae extraction: DONE Minutiae post-processing: DONE

Matching

Minutiae tuple matching (Tree based): IN PROGRESS Minutiae BFMatcher score based: DONE Softmax based ranking: TO DO

Infrastructure

Main class deployment: DONE API Development: IN PROGRESS Documentation: IN PROGRESS Unit-testing: IN PROGRESS Requirements: DONE

Prerequisites

• Python 3.6+
• Install required dependencies from requirements.txt

Execution

• Call FingerMatcher using the preferred matcher: 'tree' (in development) or 'bf'
• Use FingerMatcher functions for desired functionality.

1. loadData(path, image_format)

Loads the given image format data from a given root path. The function recursively iterates the path and loads raw images, applies histogram equalisation, binarisation, dot removal, enhancement for ridge smoothing and repair.

2. trainData()

Extracts minutiae from the loaded data based on either Harris edges or the crossing numbers approach (Default). The minutiae and tuple profile of images (tree matching) or the descriptors (BFMatcher)

3. matchFingerprint(image_test, verbose, match_th)

Uses the extracted minutiae from each base image for drawing a comparison between them and the test image given. The matching is done based on the selected model. It can be done either via BFMatcher or via tree matching. match_th describes the threshold for the BFMatcher. Not considered for tree matching at the moment. The function returns the scores list and some information about a match or not based on the given threshold. If no threshold is given, it outputs a boolean prediction.

Authors

• Dragos Tudor (dragostudoriliuta@gmail.com)