fingerflow 0.0.5

FingerFlow

FingerFlow

FingerFlow is an end-to-end deep learning Python framework for fingerprint minutiae manipulation built on top of Keras - TensorFlow high-level API.

In current stable version 1.0.0 following modules are provided:

• extractor - module responsible for extraction and classification of minutiae points from fingerprints

GPU support

FingerFlow supports GPU acceleration on CUDA®-enabled graphic cards.

Software requirements

• Python 3.9 or newer
• CUDA - for TensorFlow GPU acceleration (if missing, CPU will be used for computation)

Installation

Use the package manager pip to install FingerFlow. We reccomend to use it in pip or anaconda enviroment.

Installation in anaconda enviroment:

pip install fingerflow

Modules

Extractor

Module responsible for extraction and classification of fingerprint minutiae points. Extractor is built using MinutiaeNet neural network architecture.

Extractor contains 2 modules:

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

Neural networks models

• CoarseNet: Googledrive || Dropbox
• FineNet: Googledrive || Dropbox
• ClassifyNet: Googledrive

API

Extractor

Class which provides all functionality for extraction of minutiae points

fingerflow.extractor.Extractor()

Arguments

• coarse_net_path - used for setting path to pretrained model of submodule CoarseNet
• fine_net_path - used for setting path to pretrained model of submodule FineNet
• classify_net_path - used for setting path to pretrained model of submodule ClassifyNet

Methods

• extract_minutiae(image_data) - used for extracting minutiae points from input RGB image data. Methods accepts input data in form of numpy array. Function returns numpy ndarray of extracted and classified minutiae points in following form:
  • x - x coordinate of minutiae point
  • y - y coordinate of minutiae point
  • angle - direction of minutiae point rotation
  • score - minutiae point extraction confidence
  • class - type of minutiae point. In FingerFlow 1.0.0 we support following minutiae classes:
    • ending
    • bifurcation
    • fragment
    • enclosure
    • crossbar
    • other

Usage

import cv2
import numpy as np
from fingerflow.extractor import Extractor

extractor = Extractor("coarse_net", "fine_net", "classify_net")

image = np.array(cv2.imread("some_image"))

extracted_minutiae = extractor.extract_minutiae(image)

Contributing

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

License

MIT