Fingerprint pattern classification using Deep Learning

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Fingerprint, as a unique feature of each person, can be divided into different types. In this project, we identify real fingerprints pattern and classify them with convolutional neural networks(CNN).

Dataset

The dataset is the original NIST 8-Bit Gray Scale Images of Fingerprint Image Groups(FIGS). It comprises 4000 images that are originally of size 512 x 512. The image size has been reduced to 128 x 128 for computational purposes. The resized dataset can be found here

The dataset is in the format:

file_name.png
file_name.txt

For example, f0038_02.png file will have a corresponding f0038_02.txt file

There are 5 different fingerprint patterns namely:

Arch (A)
Left Loop (L)
Right Loop (R)
Tented Arch (T)
Whirl (W)

Now that we know how our dataset looks like, we can start our project.

Importing the required libraries

First things first, let us import the required libraries.

https://gist.github.com/91971843b6aa2a88b37e896b7bb9a603

Getting the data

Get the data directly into your notebook and then unzip to save it.

Please note: The dataset will be available only in the runtime. So, if the runtime is restarted, you will have to download and unzip the dataset again.

https://gist.github.com/6fece1cddf6dabc9bebe21a92383fed9

Reading the text file

Now, let us read the text file and save the necessary information to a dataframe.

https://gist.github.com/a3b2ac7d0570ed1579375bee8473fa42

Checking data for class imbalance

For a multi-class classification, it is important for the dataset to be balanced. By balanced, we mean that the number of inputs for each class must be close to each other. In this case, our dataset is perfectly balanced.

Here we can see that the ‘GENDER’ column is not balanced, so we can drop that column.

https://gist.github.com/aa0893dd2e1de1301bc829de98997c90