MACHINE LEARNING BASED ARTIFICIAL NEURAL NETWORKS FOR FINGERPRINT RECOGNITION
Abstract
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Fingerprint identification relies on computations and classification models based on images to identify individuals at their most basic level. For feature extraction, several image preprocessing approaches are used, and image locality bifurcations of different kinds are used for classification. For feature extraction and classification, artificial neural networks (ANNs) are proposed. ANN machine learning method and Gabor filter are introduced in this paper for feature extraction and classification respectively. Artificial Neural Networks and Gabor filtering features are used to create the feature vector. An algorithm based on the extracted features was developed to create a multiclass classifier. Special Database - NIST SD4 served as the basis for evaluation in this research. The Error matrix led to the discovery that, in terms of accuracy, the approach was superior to many traditional machine learning algorithms like Support Vector Machine, Random Forest, Decision Tree and KNN.

Authors
N.R. Pradeep, J. Ravi
Global Academy of Technology, India

Keywords
Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
200200000000
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 2 , Pages: 2874 - 2882 )
Date of Publication :
November 2022
Page Views :
297
Full Text Views :
6

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