Open AccessComparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification
Author(s) -
Hidir Noğay
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380506
Subject(s) - convolutional neural network , artificial intelligence , fingerprint (computing) , computer science , pattern recognition (psychology) , transfer of learning , feature extraction , deep learning , feature (linguistics) , realization (probability) , process (computing) , fingerprint recognition , mathematics , philosophy , statistics , linguistics , operating system
Fingerprint pattern recognition is of great importance in forensic examinations and in helping diagnose some diseases. The automatic realization of fingerprint recognition processes can take time due to the feature extraction process in classical machine learning or deep learning methods. In this study, the effective use of deep convolutional neural networks (DCNN) in fingerprint pattern recognition and classification, in which feature extraction takes place automatically, was examined experimentally and comparatively. Five DCNN models have been designed and implemented with a transfer learning approach. Four of these five models are Alexnet, Googlenet, Resnet-18, and Squeezenet pre-trained DCNN models. The fifth model is the DCNN model designed from the ground up. It was concluded that the designed DCNN models can be used effectively in fingerprint recognition and classification, and that fast results can be obtained and generalized with advanced DCNN models.