Overview / Usage

Here, I have designed a Convolutional Neural Network (CNN) with image inputs of size(300x300). The network is trained with a set of training images of fingerprints (13,618 in total) collected from different sensors. The data is collected in 2 sets of fake and live. The desired output, i.e, whether the fingerprint is live or fake, is given by the trained network. I have tried to implement a SUPERVISED CLASSIFICATION learning model by using feedforward and backpropagation algorithm technique. Here I have used the KERAS library which is already written in Python.

Methodology / Approach

https://github.com/souvikbaruah/CNN-Model-Detecting-Liveness-of-Fingerprint-using-Deep-Learning-/blob/master/README.md

Technologies Used

Python, Keras, CNN, Neural Networks, VGG16

Repository

https://github.com/souvikbaruah/CNN-Model-Detecting-Liveness-of-Fingerprint-using-Deep-Learning-

Other links

• VGG16 Architecture : Comparing Performance of the CNN Model (https://github.com/souvikbaruah/CNN-VGG16-Model-Detecting-Liveness-of-Fingerprint-using-Deep-Learning)