FingerNet : A Universal Deep Convolutional Network for Extracting Fingerprint Representation

By Yao Tang, Fei Gao, JuFu Feng and YuHang Liu at Peking University

Introduction

FingerNet is a universal deep ConvNet for extracting fingerprint representations including orientation field, segmentation, enhanced fingerprint and minutiae. It can produce reliable results on both rolled/slap and latent fingerprints.

Here is a Python implementation of FingerNet. This code has been tested on Ubuntu 14.04 and Python2.7.

License

FingerNet is released under the MIT License (refer to the LICENSE file for details).

Citing FingerNet

If you find FingerNet useful in your research, please consider citing:

@inproceedings{tang2017FingerNet,
    Author = {Tang, Yao and Gao, Fei and Feng, Jufu and Liu, Yuhang},
    Title = {FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction},
    booktitle = {Biometrics (IJCB), 2017 IEEE International Joint Conference on},
    Year = {2017}
    organization={IEEE}
}

Main Results

training data	test data	precision	recall	conv-time/img	post-time/img
CISL24218	FVC2004	76%	80%	674ms	285ms
CISL24218	NISTSD27	63%	63%	183ms	885ms

Note: 0. conv-time/img can be faster if using batch size greater than 1. 0. post-time/img contains orientation selection, segmentation dilation and minutiae nms. 0. CISL24218 is a in-house database which is unavailable to public. It contains around 8000 matched roll and latent fingerprints with manual minutiae and around 10000 latent fingerprints with only manual minutiae. Both FVC2002DB2A and NISTSD27 are non-intersect with CISL24218.

Requirements: software
Requirements: hardware
Predicting Demo
Preparation for Training & Testing
Training
Testing
Acknowledgement

Requirements: software

Python 2.7: cv2, numpy, scipy, matplotlib, pydot, graphviz
Tensorflow 1.0.1
Keras 2.0.2

Requirements: hardware

GPU: Titan, Titan Black, Titan X, K20, K40, K80.

FingerNet predicting
- 2GB GPU memory for FVC2002DB2A
- 5GB GPU memory for NISTSD27

Predicting Demo

Run cd in shell to directory src/

Run python train_test_deploy.py 0 deploy to test demo images provided in datasets/.

You may use different GPU by changing 0 to desired GPU ID.
You will see the timing information as below. We get the following running time on single-core of K80 the demo images:

Predicting images:
images 1 / 1: B101L9U
load+conv: 4.872s, seg-postpro+nms: 0.223, draw: 2.249
Average: load+conv: 4.872s, oir-select+seg-post+nms: 0.223, draw: 2.249
Predicting CISL24218:
CISL24218 1 / 1: A0100003009991600022036_2
load+conv: 2.219s, seg-postpro+nms: 0.439, draw: 2.247
Average: load+conv: 2.219s, oir-select+seg-post+nms: 0.439, draw: 2.247
Predicting FVC2002DB2A:
FVC2002DB2A 1 / 1: 1_1
load+conv: 1.718s, seg-postpro+nms: 0.548, draw: 3.309
Average: load+conv: 1.718s, oir-select+seg-post+nms: 0.548, draw: 3.309
Predicting NIST4:
NIST4 1 / 1: F0001_01
load+conv: 1.006s, seg-postpro+nms: 1.640, draw: 3.947
Average: load+conv: 1.006s, oir-select+seg-post+nms: 1.640, draw: 3.947
Predicting NIST14:
NIST14 1 / 1: F0000001
load+conv: 6.211s, seg-postpro+nms: 3.271, draw: 4.643
Average: load+conv: 6.211s, oir-select+seg-post+nms: 3.271, draw: 4.643

The visual results might be different from those in the paper due to numerical variations.

Change deploy_set=['*/',..., '*/'] in line 44-45 in train_test_deploy.py to desired dataset folders to test other fingerprint datasets.

Preparation for Training and Testing

Move raw fingerprint training images to datasets/dataset-name/images/
- Training images should be of bmp format
Move segmentation labels to datasets/dataset-name/seg_labels/
- Segmentation labels should be of png format and have the same size and name with its corresponding fingerprint images.
- 255 indicates foreground while 0 for background.
Move orientation labels to datasets/dataset-name/ori_labels/
- Orientation labels are rolled/slap fingerprint aligned to training images and of same size, same name and bmp format.
Move minutiae labels to datasets/dataset-name/mnt_labels/
- Minutiae labels should be of same name and mnt format.
- .mnt structure is as follow:
  - line 1: image-name
  - line 2: number-of-minutiae-N, image-W, image-H
  - next N lines: minutia-x, minutia-y, minutiae-o
Change train_set=['*/', ...'*/'] in line 42 in train_test_deploy.py to train_set=['../datasets/dataset-name/',]

Training:

Run python train_test_deploy.py 0 train to finetune your model.
- Note: Maximum epoch is set to 100. Early stop if model have converged.

Testing

Run python train_test_deploy.py 0 test to test your model.
- Different from Predicting, Testing requires datasets to have at least mnt labels and segmentation labels.
- Change test_set=['*/', ...'*/'] in line 44 in train_test_deploy.py to test other datasets.

manuelaguadomtz/FingerNet