The Mathematical Model and Deep Learning Features Selection for Whorl Fingerprint Classifications

In this paper, different classes of the whorl fingerprint are discussed. A general dynamical system with a parameter theta is created using differential equations to simulate these classes by varying the value of theta. The global dynamics is studied, and the existence and stability of equilibria are analyzed. The Maple is used to visualize fingerprint's orientation image as a smooth deformation of the phase portrait of a planar dynamical system. In general, the databases of fingerprint are not categorized to retained by artificial intelligence tools such Convolutional Neural Networks (CNNs) architectures, so finding a dynamical system to categorize fingerprint database of fingerprints images allows CNNs architectures to retrained with more accuracy. NIST Special Database (SD) 302d fingerprint dataset is retrained over VGG16 as CNN architecture. (C) 2021 The Authors. Published by Atlantis Press B.V.