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Minutiae extraction and classification tool

Project description


MinutiaeClassificator is a Python library for extracting and classifiying minutiae from fingerprint images.

MinutiaeClassificator contains 2 modules:

  • MinutiaeNet - module responsible for extracting minutiae points from fingerprint image. Using neural network architecture from MinutiaeNet
  • ClassifyNet - module responsible for classifying extraced minutiae points. Architecture based on FineNet module of MinutiaeNet

Requirements: software

  • Python 2.7 - we are planning to update module for Python 3.x in future
  • CUDA - MinutiaeClassificator using TensorFlow GPU acceleration


Use the package manager pip to install foobar. We reccomend to use it in anaconda enviroment. Installation in anaconda enviroment:

conda install cudatoolkit=<version compatible with the system CUDA>
pip install minutiaeclassificator


Import modules

  • MinutiaeClassificator.exceptions.MinutiaeClassificatorExceptions - module containing library specific exceptions:

    • CoarseNetPathMissingException
    • FineNetPathMissingException
    • ClassifyNetPathMissingException
    • MinutiaeNetNotLoadedException
    • ClassifyNetNotLoadedException
  • MinutiaeClassificator.MinutiaeClassificatorWrapper - main module contains library module MinutiaeClassificator. It is main module for accessing library. It contains methods:

    • get_coarse_net_path(coarse_net_path) - used for setting path to pretrained model of submodule CoarseNet.
    • get_fine_net_path(fine_net_path) - used for setting path to pretrained model of submodule FineNet.
    • get_classify_net_path(classify_net_path) - used for setting path to pretrained model of submodule ClassifyNet.
    • load_extraction_module() - used for compiling extraction module MinutiaeNet. Throws CoarseNetPathMissingException or FineNetPathMissingException when missing path to respective model's weights file
    • load_classification_module() - used for compiling classification module ClassifyNet. Throws ClassifyNetPathMissingException when missing path to its weights file
    • get_extracted_minutiae(image_path, as_image = True) - used for extracting minutiae points from input image. Image is determined by image_path (path to image file). When as_image = True minutiae points are marked in input image and updated image is returned as PIL.Image. If as_image = False minutiae_points are returned as numpy.array. If MinutiaeNet not loaded throws MinutiaeNetNotLoadedException
    • get_classified_minutiae(image_path, extracted_minutiae, as_image = True) - used for classifying extracted minutiae points. Accepts same arguments as previous method and additionaly extracted_minutiae, which is numpy.array in same shape as get_extracted_minutiae output. If ClassifyNet not loaded throws ClassifyNetNotLoadedException
    • get_extracted_and_classified_minutiae(image_path, as_image = True) - wrapper over previous two methods. Extracts and then classify extracted minutiae.
    • get_single_classified_minutiae(minutiae_patch_path) - used for classification of singe minutiae point image. Image is determined by minutiae_patch_path(path to image file). In version 1.0.0, library is able to classify minutiae points into 6 classes:
      • ending
      • bifurcation
      • fragment
      • enclosure
      • crossbar
      • other



from MinutiaeClassificator.MinutiaeClassificatorWrapper import MinutiaeClassificator
from MinutiaeClassificator.exceptions.MinutiaeClassificatorExceptions import 

minutiaeClassificator = MinutiaeClassificator()
minutiaeClassificator.get_classify_net_path('path to file')

except ClassifyNetPathMissingException:
    do something...


Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.



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